Abstract
Background
Cardiometabolic multimorbidity (CMM) is prevalent among individuals with hypertension. Although insulin resistance (IR)-related indices have been linked to CMM, their associations with incident CMM in hypertensive populations remain unclear. This study examined the associations in this high-risk group, focusing on the roles of genetic factors and biomarkers.
Methods
This observational prospective cohort analysis used data from the UK Biobank, comprising 129,853 hypertensive patients free of pre-existing coronary heart disease, stroke, or type 2 diabetes. Eight IR-related indices were computed: triglyceride-glucose (TyG) index, TyG-body mass index (TyG-BMI), TyG-waist circumference (TyG-WC), TyG-waist-to-height ratio (TyG-WHtR), TyG-a body shape index (TyG-ABSI), TyG-weight-adjusted waist index (TyG-WWI), TyG-body roundness index (TyG-BRI), and triglyceride to high-density lipoprotein cholesterol ratio (TG-HDL-C). Associations were examined using Cox proportional hazards models. Incremental predictive value was quantified using the net reclassification improvement, integrated discrimination improvement, and the C index. We also analyzed their joint and interaction effects with genetic risk and conducted exploratory biomarker analyses.
Results
Over a median follow-up of 13 years, 28,455 incident CMM events were recorded. Multivariable-adjusted hazard ratios (95% CIs) the highest versus lowest tertiles were: 1.78 (1.73–1.83) for TyG index, 2.10 (2.04–2.17) for TyG-BMI, 2.29 (2.21–2.37) for TyG-WC, 2.23 (2.16–2.30) for TyG-WHtR, 2.08 (2.02–2.15) for TyG-WWI, 2.17 (2.10–2.24) for TyG-BRI, 1.90 (1.84–1.97) for TyG-ABSI, and 1.73 (1.68–1.79) for TG-HDL-C. Per standard deviation increment was associated with 18%-48% higher risks. All indices improved incremental predictive value, with TyG-WHtR showing the strongest performance. Individuals with high IR-related indices and high genetic risk exhibited the highest CMM risk, with additive interactions. High genetic risk appeared to strengthen the adverse associations between IR-related indices and CMM, with evidence of multiplicative interactions. Biomarker analyses suggested that systemic inflammation and biomarkers of liver and renal function might statistically account for part of the observed associations.
Conclusion
Higher levels of IR-related indices, especially TyG-WHtR, were associated with an elevated risk of incident CMM in individuals with hypertension, particularly among those with high genetic risk. Inflammatory, hepatic, and renal biomarker abnormalities were related to the associations. IR-related indices, particularly TyG-WHtR, may provide useful information for risk stratification of CMM among individuals with hypertension.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12933-026-03154-8.
Keywords: Hypertension, Insulin resistance, Triglyceride glucose, Cardiometabolic multimorbidity, Genetic risk, Joint, Interaction, Biomarker, Mediation, Cohort study
Research insights
What is currently known about this topic?
Hypertension is associated with increased CMM risk.
IR-related indices are validated predictors of CMM risk.
Their associations with CMM in hypertension is unclear.
What is the key research question?
Do IR-related indices predict CMM risk in hypertension?
What role does genetic risk play in the associations?
What are the potential biological process for the associations?
What is new?
IR-related indices, especially TyG-WHtR, predict incident CMM in hypertension.
High genetic risk and high IR indices jointly elevate CMM risk, with additive and multiplicative interactions.
Inflammatory, liver, and kidney markers were related the associations.
How might this study influence clinical practice?
IR-related indices may improve CMM risk stratification in hypertension, particularly among those with high genetic risk.
Introduction
With the rapid aging of the global population, multimorbidity has emerged as a major public health challenge, particularly cardiometabolic multimorbidity (CMM). CMM, defined as the coexistence of two or more cardiometabolic diseases (CMDs), including hypertension, type 2 diabetes (T2D), coronary heart disease (CHD), and stroke, represents one of the most prevalent and severe forms of multimorbidity [1]. Accumulating evidence indicates that CMM is associated with a substantial increase in all-cause mortality and a significant reduction in life expectancy, far exceeding the impact of individual CMDs [1, 2]. Hypertension, the most prevalent chronic disease globally [3], plays a pivotal role in the development of CMM and is frequently comorbid with other cardiometabolic disorders [4, 5]. A population-based study revealed that a significant proportion of individuals with diabetes, cardiovascular disease (CVD), or chronic kidney disease also have hypertension [4], and nearly one-quarter of hypertensive patients eventually develop CMM [6]. Importantly, the presence of additional cardiometabolic conditions in hypertensive individuals markedly elevates mortality risk [7]. Given the high prevalence of hypertension and its strong contribution to CMM, identifying simple metabolic markers for CMM in this population is essential for early risk stratification and the implementation of effective preventive strategies.
Hypertension is frequently accompanied by metabolic disturbances, among which insulin resistance (IR) plays a pivotal role in the underlying pathophysiological processes [8, 9]. IR, defined as a reduced biological response to insulin in peripheral tissues, impairs glucose uptake and disrupts metabolic homeostasis. It is well-established as a key contributor to CMDs and increased mortality risk [10–12]. Mechanistically, IR may initiate a cascade of harmful processes, including sympathetic nervous system over-activation, endothelial dysfunction, chronic low-grade inflammation, oxidative stress, and dysregulated lipid metabolism, which collectively accelerate the onset of cardiometabolic disorders and premature death [10–13]. Despite its clinical relevance, direct assessment of IR remains challenging in routine practice. While the hyperinsulinemic-euglycemic clamp is considered the gold standard, its invasiveness, technical complexity, and high cost limit its application to research settings [14]. As a result, several simple and accessible surrogate markers have been developed, including the triglyceride-glucose (TyG) index and its anthropometric derivatives, such as TyG-body mass index (TyG-BMI), TyG-waist circumference (TyG-WC), TyG-waist-to-height ratio (TyG-WHtR), TyG-body roundness index (TyG-BRI), TyG-a body shape index (TyG-ABSI), and TyG-weight-adjusted waist index (TyG-WWI), as well as lipid-based indices such as the triglyceride-to-high-density lipoprotein cholesterol ratio (TG-HDL-C) [15–23]. Despite the growing body of evidence linking IR-related indices to CMM, important knowledge gaps remain. First, most previous studies have been conducted in the general population [24–43] or in individuals with cardiovascular-kidney-metabolic (CKM) syndrome [44], whereas evidence specifically addressing incident CMM in individuals with hypertension remains largely unclear. To our knowledge, only one previous study evaluated the associations between metabolic indices and CMM in patients with hypertension [45]. Still, this study did not use IR-related indices such as the TyG index and its anthropometric derivatives as the exposures [45]. Second, several studies conducted in hypertensive populations have investigated IR-related indices mainly in relation to individual cardiovascular or mortality outcomes [17, 46, 47], rather than the incidence of CMM. Therefore, whether IR-related indices are associated with the risk of incident CMM among individuals with hypertension remains largely unclear. This question is clinically important because hypertension is one of the most common entry points into the trajectory of CMM, and identifying simple metabolic markers in this high-risk population may improve early risk stratification and prevention. Third, prior studies examining this topic have often evaluated only a single IR-related index or a limited number of indices [24–43], and few studies have systematically examined multiple IR-related indices in individuals with hypertension. Given that different indices combine lipid-glucose metabolism with various anthropometric indicators and may capture distinct aspects of metabolic dysfunction, their predictive performance may differ across populations and disease outcomes [21, 33, 48, 49]. Therefore, a systematic comparison of multiple IR-related indices within the same hypertensive population may help identify which index is most informative for assessing CMM risk. Furthermore, although IR-related indices may be linked to CMM through chronic inflammation and multi-organ dysfunction, including hepatic and renal impairment [38], the biomarker patterns related to these associations have not been well characterized in hypertensive individuals. Investigating these biomarkers may provide additional insights into the pathophysiological processes potentially related to the association between IR and CMM in hypertensive populations. Furthermore, the development of CMDs is influenced by the complex interplay between genetic risk and environmental factors [50, 51]. Although previous studies have demonstrated that favorable metabolic profiles can partially offset the elevated risk of CVD associated with high genetic risk [20, 52], whether IR-related indices jointly act with or interact with genetic risk in shaping the risk of CMM among hypertensive individuals remains unclear. Addressing these issues may help refine both risk stratification and the understanding of biomarker profiles associated with these relationships.
Taken together, current evidence remains insufficient to clarify the comparative associations, predictive utility, biomarker patterns statistically related to these associations, and genetic interplay of IR-related indices in relation to incident CMM among individuals with hypertension. Therefore, using data from the UK Biobank cohort, we conducted a prospective observational study among individuals with hypertension to address these gaps. The objectives were to: (1) investigate the associations between eight IR-related indices and incident CMM among individuals with hypertension; (2) compare their incremental predictive value beyond traditional risk factors; (3) explore the joint and interactive effects of IR-related indices and genetic risk; and (4) conduct exploratory, hypothesis-generating analyses of inflammatory, hepatic, and renal biomarkers that might statistically account for part of the associations. By addressing these aims, our study sought to provide a more comprehensive evaluation of IR-related indices for CMM risk stratification in hypertensive populations.
Methods
Study participants
This observational prospective cohort analysis utilized data from the UK Biobank, a large, well-characterized prospective cohort of over 500,000 middle-aged and older adults, recruited between 2006 and 2010 across England, Scotland, and Wales. Upon enrollment, participants completed extensive questionnaires, underwent standardized physical examinations, and provided biological samples. Detailed descriptions of the cohort design and data collection protocols have been previously published [53]. Ethical approval was granted by the North West Multi-Centre Research Ethics Committee (reference: 21/NW/0157), and all participants provided written informed consent. Data for this study were accessed through the UK Biobank (application number 68136).
This analysis included 204,121 UK Biobank participants with hypertension at baseline. Following prior studies [54, 55], baseline hypertension was defined using multiple data sources (Table S1). Participants were classified as hypertensive at baseline if there was evidence of a diagnosis with a first occurrence date on or before the baseline recruitment date, as identified from primary care records, hospital inpatient data, or self-reported medical conditions using the International Classification of Diseases, 10th Revision (ICD-10) codes (I10-I15). Participants who reported current use of antihypertensive medications at baseline were also classified as hypertensive. Additionally, participants were considered hypertensive if their baseline blood pressure measurements showed a systolic blood pressure (SBP) ≥ 140 mmHg or a diastolic blood pressure (DBP) ≥ 90 mmHg. Subsequently, individuals with missing IR-related indices (n = 30,160), baseline cardiometabolic diseases (CHD, stroke, or T2D) (n = 27,771), missing genetic risk data (n = 1,914), or missing covariate information (n = 14,423) were excluded sequentially. The final study cohort comprised 129,853 participants with hypertension at baseline (Figure S1). The inclusion criteria were designed to comprehensively capture individuals with hypertension at baseline using multiple sources. These exclusions were necessary to avoid misclassification of exposure or outcome, ensure the assessment of incident CMM, and allow complete adjustment for confounders in multivariable models.
IR-related indices
Peripheral venous blood samples were collected by the UK Biobank from randomly selected participants who provided written informed consent. After fractionation and thorough quality control, biospecimens were stored at − 80 °C and later used to measure several biochemical markers, including triglycerides (TG), glucose, and high-density lipoprotein cholesterol (HDL-C). All assays were performed in a central laboratory using a Beckman Coulter AU5800 automated analyzer, with coefficients of variation below 3% for both TG and glucose [56]. Detailed protocols have been described elsewhere [53, 56]. Baseline anthropometric measurements, including height, weight, and WC, were also recorded. Eight IR-related indices at baseline were developed: the TyG index, TyG-BMI, TyG-WC, TyG-WHtR, TyG-BRI, TyG-ABSI, TyG-WWI, and TG-HDL-C. Since TG and glucose concentrations in the UK Biobank are measured in mmol/L, values were converted to mg/dL using standard conversion factors before calculating these indices [15]. As outlined in previous studies [16–23], these IR-related indices were computed as follows:
TyG index = ln [TG (mg/dL) × glucose (mg/dL)/2];
TyG-BMI =
;TyG-WC =
;TyG-WHtR =
;TyG-ABSI =
;TyG-WWI =
;TyG-BRI =
[
];TG-HDL-C = TG (mg/dL)/HDL-C (mg/dL).
Assessment of blood biomarkers
Informed by previously proposed biological pathways [17, 20, 38], we incorporated a range of blood biomarkers to explore their potential mediating roles in the relationship between IR-related indices and incident CMM among individuals with hypertension. Selected biomarkers were grouped into liver-related (albumin, alanine aminotransferase [ALT], aspartate aminotransferase [AST], alkaline phosphatase [ALP], direct bilirubin (DB), gamma-glutamyltransferase [GGT], and total protein [TP]), kidney-related (urea, creatinine, cystatin C, phosphate, and urate), and inflammation-related measures (C-reactive protein [CRP], white blood cell [WBC], lymphocyte, monocyte, neutrophil, eosinophil, basophil, and platelet counts). Detailed information on these biomarkers is presented in Table S2.
Identification of CMM
The outcome of interest in this study was the incidence of CMM among individuals with hypertension. CMM is typically defined as the coexistence of two or more CMDs, including CHD, stroke, and T2D [1]. In the present study, because all participants had hypertension at baseline, incident CMM was defined as the first occurrence of at least one additional CMD during follow-up, representing the transition from hypertension alone to CMM (hypertension plus at least one additional CMD), consistent with previous studies [54, 57]. We acknowledge that, in this context, incident CMM may reflect disease progression or complication in individuals with pre-existing hypertension. Incident CMD events were identified using the UK Biobank “First occurrence” data fields (Category ID: 1712), which integrates diagnostic information from self-reported medical conditions, primary care records, hospital inpatient data, and registry records. Disease diagnoses were determined using the ICD-10 codes, including E11 for T2D, I60-I64 and I69 for stroke, and I20-I25 for CHD. The incidence date of CMM was defined as the earliest diagnosis date of any of the above CMDs during the follow-up period. Follow-up time was calculated from the baseline recruitment date to the date of CMM incidence, death, loss to follow-up, or the censoring date (February 27, 2022), whichever occurred first. Participants lost to follow-up were censored at their last date of available follow-up.
Assessment of genetic risk
Whole-genome genotyping was performed on blood samples from participants, following previously described protocols for genotyping and quality control [58]. The genotyping used the UK Lung Exome Variant Evaluation (UK BiLEVE) array or the UK Biobank Axiom Array. The UK Biobank has made available standard polygenic risk scores (PRSs) for 53 diseases and traits, which provide strong capacity for risk stratification [59]. The genetic risk for each CMD was evaluated using the “Standard PRS” (Category ID: 301). To quantify the genetic risk of incident CMM, a composite polygenic risk score (cPRS) was developed. First, the PRSs for each CMD were standardized to z-scores for comparability. These standardized scores were then included in a Cox proportional hazards model, with incident CMM as the outcome. The regression coefficients from the model were used as weights, reflecting each disease’s genetic contribution to CMM risk. The cPRS was calculated as the weighted sum of the PRSs for each trait, with higher values indicating greater genetic risk. Participants were classified into three groups—low, moderate, and high genetic risk—based on the tertile distribution of the cPRS.
Covariates
Covariates were selected a priori based on prior studies and biological plausibility that may be associated with both IR-related indices and cardiometabolic outcomes [20, 21, 23, 38, 54]. Specifically, these covariates were chosen from several domains: demographic factors [age (continuous, in years), sex, and ethnicity (White vs. non-White)], which are well-established determinants of IR, adiposity, and cardiometabolic risk [60–62]; socioeconomic factors [Townsend Deprivation Index (TDI; continuous), employment status (employed vs. unemployed), and education level (college degree or higher vs. lower)], which may influence both metabolic status and cardiometabolic outcomes [63, 64]; lifestyle factors [smoking status (never, ever, or current), alcohol consumption (never to daily/almost daily), physical activity (sufficient vs. insufficient), and sleep duration (7–8 h/day vs. < 7 or > 8 h/day)], which are known to affect IR and cardiometabolic risk [51]; and clinical factors [SBP, DBP, glycated hemoglobin (HbA1c, continuous, in mmol/L), and low-density lipoprotein cholesterol (LDL-C, continuous, in mg/dL)], which are closely related to metabolic dysfunction and CMDs [65]. Smoking status was defined according to the UK Biobank baseline variable on current/past tobacco smoking: participants reporting ‘Never’ were classified as never smokers, those reporting ‘Past’ were classified as ever smokers, and those reporting ‘Current’ were classified as current smokers. Alcohol consumption was defined according to the UK Biobank baseline question on drinking frequency and categorized as ‘Never,’ ‘Special occasions only,’ ‘1–3 times/month,’ ‘1–2 times/week,’ ‘3–4 times/week,’ or ‘Daily/almost daily.’ The TDI is an indicator of area-level socioeconomic deprivation, with higher values indicating greater deprivation [66]. Sufficient physical activity was defined as ≥ 75 min/week of vigorous-intensity activity, ≥ 150 min/week of moderate-intensity activity, or an equivalent combination [67]. Participants with missing covariate data were excluded from the analysis, as the proportion of missing data was low (typically < 5%) (Table S3). Detailed definitions of covariates are listed in Table S4.
Statistical analyses
Baseline descriptive analyses
Baseline characteristics were summarized using appropriate descriptive statistics and stratified by CMM status and tertiles of the TyG index (T1-T3). Normality of continuous variables was assessed using the Shapiro–Wilk test. Normally distributed variables were presented as means (standard deviation, SD), non-normally distributed variables as medians (interquartile ranges, IQR), and categorical variables as frequencies and proportions. Between-group differences were evaluated using Student’s t test, Kruskal–Wallis test, or chi-square test, as appropriate.
Primary association analyses
To investigate the associations between IR-related indices and incident CMM, Cox proportional hazards models were applied, using follow-up time as the underlying timescale. The proportional hazards assumption was assessed using Schoenfeld residual tests, and no significant violations were observed (all P > 0.05) (Table S5). Hazard ratios (HRs) with 95% confidence intervals (CIs) were reported for both continuous (per SD increase) and categorical (T1 [reference], T2, and T3) analyses of IR-related indices. Each IR-related index was entered into the Cox model separately rather than simultaneously, to evaluate its association with incident CMM. Specifically, because these indices are mathematically related and showed moderate to high correlations with one another (Table S6), they were not included together in the same model. Nevertheless, the correlations were not complete, suggesting that these indices capture overlapping but not identical aspects of IR and metabolic dysfunction. Accordingly, evaluating their associations with cardiometabolic outcomes in separate models is informative and represents a commonly used analytical approach [20, 21, 23, 38]. The cumulative hazard of incident CMM across tertiles of IR-related indices was compared using the Kaplan–Meier (KM) method and tested with the log-rank test. Three Cox models were developed: Model 1 adjusted for age, sex, race, employment status, educational level, and Townsend Deprivation Index (TDI); Model 2 included additional adjustments for smoking status, drinking frequency, sleep duration, and physical activity; and Model 3 further adjusted for SBP, DBP, HbA1c, and LDL-C. Multicollinearity among covariates was evaluated using variance inflation factors (VIFs). All VIF values were < 1.38, indicating no evidence of substantial multicollinearity. To assess dose–response relationships, restricted cubic spline (RCS) functions based on Cox models were used, adjusted for the covariates in Model 3.
Incremental predictive value analyses
To quantify the incremental predictive value of IR-related indices for incident CMM beyond established risk factors among individuals with hypertension, two nested prediction models were constructed in accordance with previous studies [20, 21, 23, 38]. The conventional (reference) model included all covariates adjusted in Model 3, while the extended model additionally incorporated IR-related indices separately. In the analyses, IR-related indices were modeled as continuous variables, consistent with previous studies [20, 21, 23, 38], because the main objective was to compare the relative incremental predictive value of different indices beyond conventional risk factors rather than to identify clinically meaningful thresholds. This approach can preserve the full range of information, maximize statistical power, and allow direct comparison of improvements in predictive performance across indices. Incremental model performance was evaluated using the concordance index (C-index), net reclassification index (NRI), and integrated discrimination improvement (IDI) [68]. Discriminative ability was assessed using the C-index, whereas NRI and IDI were used to quantify improvements in risk reclassification and discrimination achieved by the extended model relative to the conventional model [57]. To compare predictive performance among IR-related indices, we examined the magnitude of improvement in these metrics for each extended model. Moreover, pairwise comparisons of C-index values were further conducted between the top-performing indices based on improvements in C-index, NRI, and IDI. These analyses were intended as comparative and exploratory assessments of relative predictive utility, rather than formal model development or validation.
Joint and interaction analyses with genetic risk
To evaluate the joint effects of genetic risk and IR-related indices on incident CMM, nine mutually exclusive exposure groups were defined by cross-classifying genetic risk categories with tertiles of IR-related indices. Participants with low genetic risk and IR-related indices in the lowest tertile (T1) served as the reference group. Stratified analyses were further conducted to estimate associations between IR-related indices and incident CMM within each genetic risk category. Potential effect modification by genetic risk was assessed by including multiplicative interaction terms between IR-related indices and genetic risk in Cox proportional hazards models adjusted for covariates in Model 3 [69]. Statistical significance of multiplicative interactions was evaluated using likelihood ratio tests [69]. Additive interactions were examined by calculating the relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index (SI) [70]. Evidence of additive interaction was considered present when the 95% confidence interval (CI) for RERI or AP did not include 0, or when the CI for SI did not include 1 [70].
Exploratory analyses of biomarkers
Exploratory, hypothesis-generating biomarker analyses were undertaken to investigate whether blood biomarkers measured at baseline could statistically account for part of the associations between baseline IR-related indices and incident CMM during follow-up among individuals with hypertension. Consistent with previous analytical frameworks [38, 71, 72], a two-stage screening strategy was applied to identify candidate blood biomarkers. In the first stage, multivariable-adjusted linear regression models were used to examine associations between IR-related indices and individual biomarkers measured at baseline. In the second stage, Cox proportional hazards models with adjusted covariates in Model 3 were employed to assess the relationships between these baseline biomarkers and the risk of incident CMM. Biomarkers that demonstrated statistically significant associations with IR-related indices and CMM, with consistent directions of effect across both stages, were selected for inclusion in the further analyses [72, 73]. These analyses were conducted using the “mediation” package in R, with the proportion mediated (PM) and corresponding 95% CIs calculated based on 1,000 nonparametric bootstrap resamples. All biomarker variables were standardized prior to analysis. Each biomarker was evaluated in a separate mediation model rather than simultaneously included in a single model. Because both IR-related indices and candidate biomarkers were measured only at baseline, these analyses do not establish the temporal ordering and should not be interpreted as evidence of causal mediation or biological pathways. Instead, they were intended only to identify biomarkers that were statistically related to the observed association. The findings should therefore be interpreted as correlational and hypothesis-generating. Moreover, the interpretation of causal mediation in this observational setting further relies on several assumptions, including no unmeasured confounding of the exposure-outcome, exposure-mediator, and mediator-outcome relationships, and no mediator-outcome confounders that are themselves affected by the exposure. These assumptions may not be fully testable in the present study; therefore, the findings should be interpreted with caution.
Sensitivity and subgroup analyses
Several sensitivity analyses were performed to examine the stability of the study results. First, to mitigate potential reverse causation, analyses were repeated after excluding participants who developed CMM within the first two years and, separately, within the first five years of follow-up. Second, the primary association analyses were re-conducted after accounting for missing covariate data using multiple imputation. Five imputed datasets were created with 20 iterations to achieve adequate convergence and robustness of the imputation procedure, and estimates from Cox proportional hazards models were combined across datasets using Rubin’s rules [74]. Third, to address the potential concern that the associations of IR-related indices with CMM might be driven by the inclusion of T2D in the outcome definition, we repeated the association analyses excluding T2D from the CMM outcome and restricting the endpoint to cardiovascular events. Fourth, we conducted an additional sensitivity analysis defining incident CMM as the occurrence of two or more CMDs during follow-up. Fifth, to further assess the potential impact of loss to follow-up, we excluded these individuals. Sixth, we further adjusted for baseline use of antihypertensive, lipid-lowering, and hypoglycemic medications to examine the potential impact of medication use. Subgroup analyses were further undertaken according to age, sex, ethnicity, educational level, employment status, TDI, smoking status, alcohol consumption frequency, physical activity level, sleep duration, and use of anti-hypertensive medications. Furthermore, formal interaction tests were performed by including multiplicative interaction terms between each IR-related index and subgroup variable in the fully adjusted models to evaluate potential effect modification.
All statistical analyses were carried out using R software (version 4.4.0). Statistical significance was determined using a two-sided P value < 0.05, with Benjamini–Hochberg false discovery rate (FDR) correction applied to the biomarker analyses and the analyses involving the eight IR-related indices to account for multiple comparisons.
Results
Baseline characteristics of study participants
Table 1 summarizes the baseline characteristics of the 129,853 participants with hypertension (median age [IQR], 60 [[53–64] years; 47.8% women), stratified according to incident CMM. Participants who developed CMM during follow-up were, on average, older and more frequently male, of non-White ethnicity, unemployed, had lower educational attainment, and resided in more socioeconomically deprived areas compared with those who remained free of CMM. They were also more likely to exhibit adverse lifestyle profiles and higher genetic risk. Moreover, individuals with incident CMM had higher baseline levels of HbA1c and IR-related indices, along with lower LDL-C concentrations (all P < 0.001). Comparable trends were observed when baseline characteristics were examined across tertiles of the TyG index (Table S7).
Table 1.
Baseline characteristics of study population stratified by incident CMM in participants with hypertension
| Total population | CMM incidence | P values | ||
|---|---|---|---|---|
| No | Yes | |||
| n | 129,853 | 101,398 | 28,455 | |
| Age (years) | 60.00 [53.00, 64.00] | 59.00 [52.00, 64.00] | 61.00 [56.00, 65.00] | < 0.001 |
| Sex | < 0.001 | |||
| Male | 67,729 (52.2) | 50,201 (49.5) | 17,528 (61.6) | |
| Female | 62,124 (47.8) | 51,197 (50.5) | 10,927 (38.4) | |
| Ethnicity | < 0.001 | |||
| White | 124,087 (95.6) | 97,257 (95.9) | 26,830 (94.3) | |
| Non-white | 5,766 (4.4) | 4,141 (4.1) | 1,625 (5.7) | |
| Employed status | < 0.001 | |||
| Employed | 120,540 (92.8) | 94,628 (93.3) | 25,912 (91.1) | |
| Unemployed | 9,313 (7.2) | 6,770 (6.7) | 2,543 (8.9) | |
| Educational level | < 0.001 | |||
| University or college | 38,096 (29.3) | 31,129 (30.7) | 6,967 (24.5) | |
| Others | 91,757 (70.7) | 70,269 (69.3) | 21,488 (75.5) | |
| Townsend deprivation index | -2.22 [-3.68, 0.35] | -2.30 [-3.72, 0.17] | -1.91 [-3.50, 0.97] | < 0.001 |
| Physical activity | < 0.001 | |||
| Adequate | 78,366 (60.3) | 62,012 (61.2) | 16,354 (57.5) | |
| Inadequate | 51,487 (39.7) | 39,386 (38.8) | 12,101 (42.5) | |
| Smoking status | < 0.001 | |||
| Never smoking | 69,341 (53.4) | 56,033 (55.3) | 13,308 (46.8) | |
| Ever smoking | 48,431 (37.3) | 36,780 (36.3) | 11,651 (40.9) | |
| Current smoking | 12,081 (9.3) | 8,585 (8.5) | 3,496 (12.3) | |
| Drinking frequency | < 0.001 | |||
| Never | 9,321 (7.2) | 6,568 (6.5) | 2,753 (9.7) | |
| Special occasions only | 14,247 (11.0) | 10,456 (10.3) | 3,791 (13.3) | |
| 1–3 times/month | 13,204 (10.2) | 10,135 (10.0) | 3,069 (10.8) | |
| 1–2 times/week | 32,092 (24.7) | 25,306 (25.0) | 6,786 (23.8) | |
| 3–4 times/week | 30,679 (23.6) | 24,812 (24.5) | 5,867 (20.6) | |
| Daily or almost daily | 30,310 (23.3) | 24,121 (23.8) | 6,189 (21.8) | |
| Sleep duration | < 0.001 | |||
| 7–8 h/day | 94,460 (72.7) | 74,699 (73.7) | 19,761 (69.4) | |
| < 7 or > 8 h/day | 35,393 (27.3) | 26,699 (26.3) | 8,694 (30.6) | |
| TyG index | 8.81 [8.45, 9.18] | 8.76 [8.42, 9.13] | 8.97 [8.59, 9.37] | < 0.001 |
| TyG-BMI index | 248.76 [219.97, 282.47] | 244.68 [216.88, 276.59] | 265.61 [233.73, 303.22] | < 0.001 |
| TyG-WC index | 830.75 [736.53, 926.07] | 815.60 [723.24, 907.33] | 888.48 [794.16, 988.68] | < 0.001 |
| TyG-WHtR index | 4.90 [4.40, 5.44] | 4.82 [4.33, 5.33] | 5.23 [4.71, 5.81] | < 0.001 |
| TyG-WWI index | 92.16 [85.60, 98.59] | 91.12 [84.68, 97.36] | 96.03 [89.59, 102.66] | < 0.001 |
| TyG-ABSI index | 0.69 [0.64, 0.73] | 0.68 [0.63, 0.72] | 0.71 [0.66, 0.76] | < 0.001 |
| TyG-BRI index | 39.18 [30.71, 49.22] | 37.72 [29.63, 47.18] | 44.85 [35.56, 56.35] | < 0.001 |
| TG-HDL-C | 2.79 [1.76, 4.43] | 2.64 [1.68, 4.17] | 3.41 [2.15, 5.28] | < 0.001 |
| HbA1c | 35.60 [33.20, 38.30] | 35.20 [32.90, 37.60] | 37.80 [34.70, 42.20] | < 0.001 |
| LDL-C | 138.79 [116.13, 162.07] | 139.95 [118.18, 162.57] | 133.84 [108.82, 159.90] | < 0.001 |
| Genetic risk | < 0.001 | |||
| Low | 43,285 (33.3) | 35,666 (35.2) | 7,619 (26.8) | |
| Moderate | 43,284 (33.3) | 34,137 (33.7) | 9,147 (32.1) | |
| High | 43,284 (33.3) | 31,595 (31.2) | 11,689 (41.1) | |
Data are presented as median (interquartile range) for continuous variables and n (%) for categorical variables
CMM, cardiometabolic multimorbidity; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; TyG, triglyceride-glucose index; BMI, body mass index; WC, waist circumference; WHtR, weight-to-height ratio; BRI, body roundness index; ABSI, a body shape index; WWI, weight-adjusted waist index; HbA1c, glycated hemoglobin; LDL-C, low-density lipoprotein cholesterol
Associations between IR-related indices and incident CMM
During a median follow-up of 13.3 years (IQR, 12.3–14.1), 28,455 participants (21.9%) developed CMM. KM analyses showed that participants in the highest tertile of IR-related indices had a significantly greater cumulative hazard of CMM than those in the lowest tertile (log-rank P < 0.001; Fig. 1). RCS analyses indicated significant nonlinear associations between IR-related indices and incident CMM (all P for nonlinearity < 0.05; Fig. 2). For most indices, the dose–response curves exhibited a J-shaped pattern (Fig. 2A–G). As shown in Table 2, all IR-related indices were positively associated with incident CMM in multivariable-adjusted Cox models (Model 3; all FDR-adjusted P < 0.001). When comparing participants in the highest versus lowest tertiles, the corresponding HRs (95% CIs) were 1.78 (1.73–1.83) for the TyG index, 2.10 (2.04–2.17) for TyG-BMI, 2.29 (2.21–2.37) for TyG-WC, 2.23 (2.16–2.30) for TyG-WHtR, 2.08 (2.02–2.15) for TyG-WWI, 2.17 (2.10–2.24) for TyG-BRI, 1.90 (1.84–1.97) for TyG-ABSI, and 1.73 (1.68–1.79) for TG-HDL-C. Among these measures, TyG-WC demonstrated the strongest association, followed by TyG-WHtR. Per SD increase in each index was associated with an 18% higher risk of CMM for TG-HDL-C, 30% for the TyG index, 35% for TyG-ABSI, 39% for TyG-BRI, 40% for TyG-BMI, 41% for TyG-WWI, 45% for TyG-WHtR, and 48% for TyG-WC.
Fig. 1.
Kaplan–Meier curves of incident CMM according to the tertiles of IR-related indices in participants with hypertension. Note A: TyG index; B: TyG-BMI index; C: TyG-WC index; D: TyG-WHtR index; E: TyG-WWI index; F: TyG-ABSI index; G: TyG-BRI index; H: TG-HDL-C. Abbreviation IR, insulin resistance; CMM, cardiometabolic multimorbidity; TyG, triglyceride-glucose; BMI, body mass index; WC, waist circumference; WHtR, waist-to-height ratio; BRI, body roundness index; ABSI, a body shape index; WWI, weight-adjusted waist index; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol
Fig. 2.
Dose–response relationship of IR-related indices with incident CMM in participants with hypertension. Note: Models were adjusted for age, sex, race, employment status, educational level, Townsend deprivation index, smoking status, drinking frequency, sleep duration, physical activity, systolic blood pressure, diastolic blood pressure, glycated hemoglobin, and low-density lipoprotein cholesterol. A: TyG index; B: TyG-BMI index; C: TyG-WC index; D: TyG-WHtR index; E: TyG-WWI index; F: TyG-ABSI index; G: TyG-BRI index; H: TG-HDL-C. Abbreviation HR, hazard ratio; CI, confidence interval; IR, insulin resistance; CMM, cardiometabolic multimorbidity; TyG, triglyceride-glucose; BMI, body mass index; WC, waist circumference; WHtR, waist-to-height ratio; BRI, body roundness index; ABSI, a body shape index; WWI, weight-adjusted waist index; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol
Table 2.
Associations between IR-related indices and incident CMM in participants with hypertension
| Exposures | Cases/Person-years | Model 1 | FDR-adjusted P | Model 2 | FDR-adjusted P | Model 3 | FDR-adjusted P |
|---|---|---|---|---|---|---|---|
| HR (95% CI) | HR (95% CI) | HR (95% CI) | |||||
| TyG index | |||||||
| Per SD increment | 28,455/1,550,560 | 1.39 (1.38–1.41) | < 0.001 | 1.37 (1.35–1.39) | < 0.001 | 1.30 (1.28–1.31) | < 0.001 |
| Tertile 1 | 6,868/536,241 | Reference | Reference | Reference | |||
| Tertile 2 | 8,763/522,080 | 1.24 (1.20–1.28) | < 0.001 | 1.21 (1.18–1.25) | < 0.001 | 1.25 (1.21–1.29) | < 0.001 |
| Tertile 3 | 12,824/492,240 | 1.90 (1.84–1.95) | < 0.001 | 1.82 (1.77–1.88) | < 0.001 | 1.78 (1.73–1.83) | < 0.001 |
| TyG-BMI index | |||||||
| Per SD increment | 1.48 (1.47–1.50) | < 0.001 | 1.45 (1.44–1.47) | < 0.001 | 1.40 (1.38–1.41) | < 0.001 | |
| Tertile 1 | 6,346/538,624 | Reference | Reference | Reference | |||
| Tertile 2 | 8,765/523,331 | 1.33 (1.28–1.37) | < 0.001 | 1.31 (1.26–1.35) | < 0.001 | 1.28 (1.24–1.32) | < 0.001 |
| Tertile 3 | 13,344/488,606 | 2.31 (2.24–2.38) | < 0.001 | 2.21 (2.14–2.28) | < 0.001 | 2.10 (2.04–2.17) | < 0.001 |
| TyG-WC index | |||||||
| Per SD increment | 1.59 (1.58–1.61) | < 0.001 | 1.56 (1.54–1.58) | < 0.001 | 1.48 (1.46–1.49) | < 0.001 | |
| Tertile 1 | 5,663/545,909 | Reference | Reference | Reference | |||
| Tertile 2 | 8,807/52,3065 | 1.49 (1.44–1.54) | < 0.001 | 1.45 (1.40–1.50) | < 0.001 | 1.42 (1.37–1.47) | < 0.001 |
| Tertile 3 | 13,985/481,586 | 2.56 (2.48–2.65) | < 0.001 | 2.43 (2.35–2.51) | < 0.001 | 2.29 (2.21–2.37) | < 0.001 |
| TyG-WHtR index | |||||||
| Per SD increment | 1.57 (1.55–1.58) | < 0.001 | 1.53 (1.52–1.55) | < 0.001 | 1.45 (1.44–1.47) | < 0.001 | |
| Tertile 1 | 5,733/544,781 | Reference | Reference | Reference | |||
| Tertile 2 | 8,645/524,441 | 1.40 (1.35–1.44) | < 0.001 | 1.37 (1.32–1.41) | < 0.001 | 1.34 (1.30–1.39) | < 0.001 |
| Tertile 3 | 14,077/481,338 | 2.47 (2.40–2.55) | < 0.001 | 2.35 (2.28–2.43) | < 0.001 | 2.23 (2.16–2.30) | < 0.001 |
| TyG-WWI index | |||||||
| Per SD increment | 1.52 (1.51–1.54) | < 0.001 | 1.49 (1.47–1.51) | < 0.001 | 1.41 (1.39–1.42) | < 0.001 | |
| Tertile 1 | 5,705/546,639 | Reference | Reference | Reference | |||
| Tertile 2 | 8,802/522,749 | 1.43 (1.38–1.48) | < 0.001 | 1.39 (1.34–1.44) | < 0.001 | 1.38 (1.34–1.43) | < 0.001 |
| Tertile 3 | 13,948/481,172 | 2.32 (2.25–2.39) | < 0.001 | 2.20 (2.13–2.27) | < 0.001 | 2.08 (2.02–2.15) | < 0.001 |
| TyG-BRI index | |||||||
| Per SD increment | 1.47 (1.45–1.48) | < 0.001 | 1.44 (1.43–1.46) | < 0.001 | 1.39 (1.37–1.40) | < 0.001 | |
| Tertile 1 | 5,745/544,668 | Reference | Reference | Reference | |||
| Tertile 2 | 8,858/523,223 | 1.42 (1.37–1.46) | < 0.001 | 1.39 (1.34–1.44) | < 0.001 | 1.35 (1.31–1.40) | < 0.001 |
| Tertile 3 | 13,852/482,669 | 2.40 (2.33–2.48) | < 0.001 | 2.29 (2.21–2.36) | < 0.001 | 2.17 (2.10–2.24) | < 0.001 |
| TyG-ABSI index | |||||||
| Per SD increment | 1.45 (1.43–1.47) | < 0.001 | 1.42 (1.40–1.44) | < 0.001 | 1.35 (1.33–1.37) | < 0.001 | |
| Tertile 1 | 5,951/545,772 | Reference | Reference | Reference | |||
| Tertile 2 | 9,180/519,921 | 1.44 (1.39–1.49) | < 0.001 | 1.41 (1.36–1.46) | < 0.001 | 1.42 (1.37–1.46) | < 0.001 |
| Tertile 3 | 13,324/484,867 | 2.10 (2.03–2.17) | < 0.001 | 2.01 (1.94–2.08) | < 0.001 | 1.90 (1.84–1.97) | < 0.001 |
| TG-HDL-C | |||||||
| Per SD increment | 1.23 (1.22–1.24) | < 0.001 | 1.20 (1.19–1.21) | < 0.001 | 1.18 (1.17–1.19) | < 0.001 | |
| Tertile 1 | 6,661/536,249 | Reference | Reference | Reference | |||
| Tertile 2 | 9,197/518,717 | 1.34 (1.30–1.38) | < 0.001 | 1.29 (1.25–1.33) | < 0.001 | 1.31 (1.27–1.35) | < 0.001 |
| Tertile 3 | 12,597/495,594 | 1.89 (1.83–1.95) | < 0.001 | 1.76 (1.70–1.81) | < 0.001 | 1.73 (1.68–1.79) | < 0.001 |
Model 1 was adjusted for age, sex, race, employment status, educational level, and Townsend deprivation index; Model 2 was adjusted for age, sex, race, employment status, educational level, Townsend deprivation index, smoking status, drinking frequency, sleep duration, and physical activity; Model 3 was adjusted for age, sex, race, employment status, educational level, Townsend deprivation index, smoking status, drinking frequency, sleep duration, physical activity, systolic blood pressure, diastolic blood pressure, glycated hemoglobin, and low-density lipoprotein cholesterol
IR, insulin resistance; CMM, cardiometabolic multimorbidity; SD, standard deviation; HR, hazard ratio; CI, confidence interval; TyG, triglyceride-glucose; BMI, body mass index; WC, waist circumference; WHtR, waist-to-height ratio; BRI, body roundness index; ABSI, a body shape index; WWI, weight-adjusted waist index; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; FDR, false discovery ratio
Incremental predictive values of IR-related indices
We next evaluated whether the addition of IR-related indices improved risk prediction for incident CMM beyond the conventional risk model in individuals with hypertension. As shown in Table 3, inclusion of each IR-related index resulted in statistically significant improvements in both the NRI and IDI for CMM prediction (all FDR-adjusted P < 0.001). Among the indices examined, TyG-WHtR and TyG-WC provided the greatest incremental predictive benefit, with TyG-WHtR yielding an NRI of 0.180 (95% CI 0.172–0.188) and an IDI of 0.033 (95% CI 0.030–0.036), and TyG-WC yielding an NRI of 0.177 (95% CI 0.169–0.184) and an IDI of 0.032 (95% CI 0.030–0.035). Consistent with these findings, C-index analyses also showed that models incorporating TyG-WHtR and TyG-WC achieved the largest improvements in discrimination (Table 3). Specifically, the C-index increased from 0.6885 (95% CI 0.6855–0.6916) in the conventional model to 0.7086 (95% CI 0.7056–0.7117) with the inclusion of TyG-WHtR, and to 0.7076 (95% CI 0.7046–0.7106) with the inclusion of TyG-WC. Overall, TyG-WHtR and TyG-WC were the two best-performing IR-related indices for incremental prediction of incident CMM. Direct comparison of C-index values between these two indices indicated that the TyG-WHtR model had a significantly higher C-index than the TyG-WC model (0.7086 vs 0.7076, ΔC = 0.0010, 95% CI 0.0006–0.0015, P < 0.001). Although the absolute difference was small, TyG-WHtR consistently showed the best overall predictive performance and was therefore selected as the single preferred index in this comparative analysis.
Table 3.
Increment predictive values of IR-related indices for the risk of incident CMM in participants with hypertension
| NRI, % | IDI, % | C-index | ||||
|---|---|---|---|---|---|---|
| Estimate (95% CI) | FDR-adjusted P value | Estimate (95% CI) | FDR-adjusted P value | Estimate (95% CI) | FDR-adjusted P value | |
| Conventional model | Reference | Reference | 0.6885 (0.6855, 0.6916) | – | ||
| Conventional model + TyG index | 0.124 (0.117, 0.132) | < 0.001 | 0.017 (0.015, 0.020) | < 0.001 | 0.6976 (0.6945, 0.7007) | < 0.001 |
| Conventional model + TyG-BMI | 0.173 (0.166, 0.181) | < 0.001 | 0.028 (0.026, 0.031) | < 0.001 | 0.7059 (0.7029, 0.7089) | < 0.001 |
| Conventional model + TyG-WC | 0.177 (0.169, 0.184) | < 0.001 | 0.032 (0.030, 0.035) | < 0.001 | 0.7076 (0.7046, 0.7106) | < 0.001 |
| Conventional model + TyG-WHtR | 0.180 (0.172, 0.188) | < 0.001 | 0.033 (0.030, 0.036) | < 0.001 | 0.7086 (0.7056, 0.7117) | < 0.001 |
| Conventional model + TyG-WWI | 0.152 (0.145, 0.159) | < 0.001 | 0.026 (0.024, 0.029) | < 0.001 | 0.7042 (0.7012, 0.7072) | < 0.001 |
| Conventional model + TyG-ABSI | 0.125 (0.117, 0.132) | < 0.001 | 0.018 (0.016, 0.020) | < 0.001 | 0.6988 (0.6957, 0.7018) | < 0.001 |
| Conventional model + TyG-BRI | 0.073 (0.164, 0.180) | < 0.001 | 0.029 (0.026, 0.031) | < 0.001 | 0.7075 (0.7045, 0.7105) | < 0.001 |
| Conventional model + TG-HDL-C | 0.103 (0.094, 0.111) | < 0.001 | 0.008 (0.006, 0.009) | < 0.001 | 0.6960 (0.6930, 0.6991) | < 0.001 |
Conventional models were adjusted for age, sex, race, employment status, educational level, Townsend deprivation index, smoking status, drinking frequency, sleep duration, physical activity, systolic blood pressure, diastolic blood pressure, glycated hemoglobin, and low-density lipoprotein cholesterol
IR, insulin resistance; CMM, cardiometabolic multimorbidity; CI, confidence interval; TyG, triglyceride-glucose; BMI, body mass index; WC, waist circumference; WHtR, waist-to-height ratio; BRI, body roundness index; ABSI, a body shape index; WWI, weight-adjusted waist index; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; NRI, net reclassification index; IDI, integrated discrimination improvement index; FDR, false discovery ratio
Joint, stratification, and interaction effects of IR-related indices and genetic risk
By jointly considering IR-related indices and genetic risk, we categorized participants into nine groups. The combined associations of IR-related indices and genetic risk with CMM incidence demonstrated a clear dose–response pattern (Fig. 3). Compared with individuals in the lowest tertile (T1) of these indices and with low genetic risk, those with high IR index levels (T3) and high genetic risk exhibited the greatest risk of incident CMM (TyG index: HR = 2.52 [95% CI 2.39–2.65]; TyG-BMI: HR = 2.99 [2.84–3.14]; TyG-WC: HR = 3.23 [3.06–3.41]; TyG-WHtR: HR = 3.06 [2.91–3.22]; TyG-WWI: HR = 2.94 [2.78–3.10]; TyG-BRI: HR = 3.02 [2.87–3.18]; TyG-ABSI: HR = 2.71 [2.57–2.87]; TG-HDL-C: HR = 2.46 [2.33–2.59]). Additive interaction analyses revealed that RERI, AP, and SI were all statistically significant and greater than zero, indicating positive additive interactions between all IR-related indices and genetic risk for CMM incidence (Table S8). In participants exposed to both high IR indices and high genetic risk, RERI values ranged from 0.334 (95% CI 0.221–0.446) to 0.762 (95% CI 0.640–0.885), while AP values ranged from 0.134 (95% CI 0.089–0.178) to 0.245 (95% CI 0.207–0.283), which suggested that the genetic-IR additive interactions contributed a relative excess risk of 0.334–0.762, accounting for 13.4%-24.5% of the CMM risk among individuals with dual high-risk exposure. Overall, these findings indicate synergistic associations between IR-related indices and genetic risk in relation to CMM incidence in individuals with hypertension.
Fig. 3.
Joint associations between IR-related indices and genetic risk and incident CMM in participants with hypertension. Note Models were adjusted for age, sex, race, employment status, educational level, Townsend deprivation index, smoking status, drinking frequency, sleep duration, physical activity, systolic blood pressure, diastolic blood pressure, glycated hemoglobin, and low-density lipoprotein cholesterol. Abbreviation HR, hazard ratio; CI, confidence interval; IR, insulin resistance; CMM, cardiometabolic multimorbidity; TyG, triglyceride-glucose; BMI, body mass index; WC, waist circumference; WHtR, waist-to-height ratio; BRI, body roundness index; ABSI, a body shape index; WWI, weight-adjusted waist index; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol
We further evaluated the associations between IR-related indices and incident CMM across genetic risk strata. As shown in Table 4, higher levels of all IR-related indices were consistently associated with an increased risk of CMM within each genetic risk category (all FDR-adjusted P < 0.001). Notably, these associations were stronger among individuals with high genetic risk compared with those with low genetic risk. Specifically, when comparing the highest versus lowest tertiles of IR-related indices, the corresponding hazard ratios were greater in the high genetic risk group than in the low genetic risk group: 1.78 (95% CI 1.70–1.87) versus 1.57 (1.48–1.66) for the TyG index; 2.18 (2.07–2.29) vs. 1.84 (1.74–1.95) for TyG-BMI; 2.41 (2.28–2.55) vs. 1.95 (1.83–2.08) for TyG-WC; 2.38 (2.26–2.51) vs. 1.90 (1.79–2.02) for TyG-WHtR; 2.15 (2.04–2.27) vs. 1.84 (1.73–1.96) for TyG-WWI; 2.29 (2.17–2.41) vs. 1.90 (1.79–2.01) for TyG-BRI; and 1.91 (1.81–2.01) vs. 1.64 (1.54–1.75) for TyG-ABSI. Further, multiplicative interaction tests indicated that genetic risk significantly modified the associations of several indices with incident CMM, including TyG-BMI, TyG-WC, TyG-WHtR, and TyG-BRI (all P for interaction < 0.01).
Table 4.
Stratified associations between IR-related indices and incident CMM according to genetic risk in participants with hypertension
| Groups | TyG index | TyG-BMI | TyG-WC | TyG-WHtR | |||||
|---|---|---|---|---|---|---|---|---|---|
| Genetic risk | IR indices | Cases/N | HR (95% CI) | Cases/N | HR (95% CI) | Cases/N | HR (95% CI) | Cases/N | HR (95% CI) |
| Low | Tertile 1 | 2,172/16,113 | 1 (Reference) | 2,132/16,579 | 1 (Reference) | 1,849/16,181 | 1 (Reference) | 1,947/16,371 | 1 (Reference) |
| Low | Tertile 2 | 2,465/14,473 | 1.17 (1.10–1.24) | 2,511/14,505 | 1.23 (1.16–1.30) | 2,461/14,367 | 1.34 (1.26–1.43) | 2,418/14,495 | 1.21 (1.14–1.28) |
| Low | Tertile 3 | 2,982/12,699 | 1.57 (1.48–1.66) | 2,976/12,201 | 1.84 (1.74–1.95) | 3,309/12,737 | 1.95 (1.83–2.08) | 3,254/12,419 | 1.90 (1.79–2.02) |
| Moderate | Tertile 1 | 2,241/14,348 | 1 (Reference) | 2,083/14,361 | 1 (Reference) | 1,865/14,348 | 1 (Reference) | 1,888/14,384 | 1 (Reference) |
| Moderate | Tertile 2 | 2,833/14,601 | 1.25 (1.18–1.33) | 2,838/14,504 | 1.25 (1.18–1.33) | 2,809/14,509 | 1.35 (1.27–1.43) | 2,786/14,471 | 1.30 (1.23–1.38) |
| Moderate | Tertile 3 | 4,073/14,335 | 1.79 (1.70–1.89) | 4,226/14,419 | 2.02 (1.91–2.13) | 4,473/14,427 | 2.19 (2.07–2.33) | 4,473/14,429 | 2.14 (2.02–2.26) |
| High | Tertile 1 | 2,455/12,824 | 1 (Reference) | 2,131/12,345 | 1 (Reference) | 1,949/12,756 | 1 (Reference) | 1,898/12,530 | 1 (Reference) |
| High | Tertile 2 | 3,465/14,210 | 1.26 (1.20–1.33) | 3,416/14,275 | 1.29 (1.22–1.37) | 3,537/14,408 | 1.48 (1.39–1.56) | 3,441/14,318 | 1.44 (1.36–1.52) |
| High | Tertile 3 | 5,769/16,250 | 1.78 (1.70–1.87) | 6,142/16,664 | 2.18 (2.07–2.29) | 6,203/16,120 | 2.41 (2.28–2.55) | 6,350/16,436 | 2.38 (2.26–2.51) |
| P for interaction | 0.268 | < 0.001 | < 0.001 | < 0.001 | |||||
| Groups | TyG-WWI | TyG-BRI | TyG-ABSI | TG-HDL-C | |||||
|---|---|---|---|---|---|---|---|---|---|
| Genetic risk | IR indices | Cases/N | HR (95% CI) | Cases/N | HR (95% CI) | Cases/N | HR (95% CI) | Cases/N | HR (95% CI) |
| Low | Tertile 1 | 1,826/16,033 | 1 (Reference) | 1,917/16,295 | 1 (Reference) | 1,786/15,503 | 1 (Reference) | 2,072/16,078 | 1 (Reference) |
| Low | Tertile 2 | 2,476/14,472 | 1.28 (1.20–1.36) | 2,436/14,404 | 1.23 (1.16–1.31) | 2,541/14,371 | 1.32 (1.24–1.40) | 2,506/14,362 | 1.21 (1.14–1.29) |
| Low | Tertile 3 | 3,317/12,780 | 1.84 (1.73–1.96) | 3,266/12,586 | 1.90 (1.79–2.01) | 3,292/13,411 | 1.64 (1.54–1.75) | 3,041/12,845 | 1.63 (1.53–1.73) |
| Moderate | Tertile 1 | 1,870/14,366 | 1 (Reference) | 1,896/14,357 | 1 (Reference) | 1,927/14,413 | 1 (Reference) | 2,171/14,385 | 1 (Reference) |
| Moderate | Tertile 2 | 2,865/14,533 | 1.33 (1.26–1.42) | 2,831/14,438 | 1.30 (1.22–1.38) | 2,986/14,588 | 1.45 (1.36–1.54) | 2,960/14,504 | 1.32 (1.25–1.40) |
| Moderate | Tertile 3 | 4,412/14,385 | 2.00 (1.89–2.12) | 4,420/14,489 | 2.06 (1.95–2.18) | 4,234/14,283 | 1.96 (1.84–2.08) | 4,016/14,395 | 1.77 (1.67–1.87) |
| High | Tertile 1 | 2,009/12,886 | 1 (Reference) | 1,932/12,633 | 1 (Reference) | 2,238/13,369 | 1 (Reference) | 2,418/12,822 | 1 (Reference) |
| High | Tertile 2 | 3,461/14,279 | 1.43 (1.35–1.51) | 3,591/14,442 | 1.45 (1.37–1.54) | 3,653/14,325 | 1.41 (1.33–1.49) | 3,731/14,418 | 1.31 (1.24–1.38) |
| High | Tertile 3 | 6,219/16,119 | 2.15 (2.04–2.27) | 6,166/16,209 | 2.29 (2.17–2.41) | 5,798/15,590 | 1.91 (1.81–2.01) | 5,540/16,044 | 1.68 (1.59–1.76) |
| P for interaction | 0.336 | 0.002 | 0.282 | 0.366 | |||||
Models were adjusted age, sex, race, employment status, educational level, Townsend deprivation index, smoking status, drinking frequency, sleep duration, physical activity, systolic blood pressure, diastolic blood pressure, glycated hemoglobin, and low-density lipoprotein cholesterol. P for interaction was calculated using likelihood ratio tests comparing Cox models with and without the interaction term between IR-related indices and genetic risk. After Benjamini–Hochberg false discovery rate correction for multiple comparisons, all P values for the association analyses remained < 0.001
IR, insulin resistance; CMM, cardiometabolic multimorbidity; HR, hazard ratio; CI, confidence interval; TyG, triglyceride-glucose; BMI, body mass index; WC, waist circumference; WHtR, waist-to-height ratio; BRI, body roundness index; ABSI, a body shape index; WWI, weight-adjusted waist index; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol
Biomarkers statistically accounting for the associations
We further performed exploratory analyses to identify biomarkers that might statistically account for part of the associations between IR-related indices and incident CMM among individuals with hypertension. We first examined the associations between IR-related indices and a panel of biomarkers, as well as the associations between these biomarkers and incident CMM in individuals with hypertension. Ultimately, 14–16 biomarkers that demonstrated statistically significant and directionally consistent associations in both linear and Cox regression models were included in the subsequent analyses (all FDR-adjusted P < 0.05; Tables S9-S17). Figure 4 and Table S18 summarize the proportion statistically accounted for by these biomarkers in the associations between IR-related indices and incident CMM (all FDR-adjusted P < 0.05). Among liver function biomarkers, ALT and GGT consistently accounted for a relatively larger proportion of the observed associations across all IR-related indices. ALT explained approximately 4.4% (95% CI 3.4–5.4%) to 10.2% (8.4–12.0%) of the associations, whereas GGT accounted for 3.5% (3.1–4.4%) to 7.1% (6.1–8.1%). In contrast, albumin, AST, and ALP showed relatively small contributions, with PM generally below 4%. Renal function-related biomarkers, including urea, creatinine, cystatin C, and urate, also statistically accounted for part of the observed associations. Among these, urate exerted the largest contribution, accounting for 7.3% (2.3–12.1%) to 20.8% (18.4–23.5%) of the associations, followed by cystatin C, which consistently explained 4.4% (3.3–6.0%) to 7.2% (5.7–9.4%), whereas creatinine and urea demonstrated modest contributions (generally < 2%). All included inflammatory markers also statistically accounted for the associations to varying degrees. Among these, WBC (3.7% to 6.6%), neutrophil count (3.7% to 6.4%), and CRP (1.8% to 3.3%) demonstrated modest contributions. These findings should be interpreted as exploratory and correlational rather than as evidence of causal biological mechanisms.
Fig. 4.
Proportion of the associations between IR-related indices and incident CMM statistically accounted for by selected biomarkers in participants with hypertension. Note: Models were adjusted for age, sex, race, employment status, educational level, Townsend deprivation index, smoking status, drinking frequency, sleep duration, physical activity, systolic blood pressure, diastolic blood pressure, glycated hemoglobin, and low-density lipoprotein cholesterol. Because IR-related indices and biomarkers were measured at the same baseline time point, these results reflect statistical mediation and should not be interpreted as evidence of temporal or causal mechanistic pathways. Abbreviation IR, insulin resistance; CMM, cardiometabolic multimorbidity; FDR, false discovery ratio; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALP, alkaline phosphatase; GGT, gamma glutamyltransferas; CRP, C-reactive protein; WBC, white blood cell count; TyG, triglyceride-glucose; BMI, body mass index; WC, waist circumference; WHtR, waist-to-height ratio; BRI, body roundness index; ABSI, a body shape index; WWI, weight-adjusted waist index; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol
Sensitivity and subgroup analyses
Additional sensitivity and subgroup analyses were performed to further evaluate the robustness of the observed associations. In sensitivity analyses, the primary findings remained materially unchanged after excluding CMM events that occurred within the first two or five years of follow-up, thereby minimizing potential reverse causation (Table S19). Similar results were obtained after applying multiple imputation to account for missing covariate data (Table S20). When T2D was excluded from the CMM definition, and the outcome was restricted to cardiovascular events, the associations between IR-related indices and incident events were attenuated compared with the primary analysis but remained significantly and positively associated with the outcome (Table S21). In the sensitivity analysis defining CMM as the occurrence of two or more CMDs during follow-up, associations with IR-related indices were largely consistent with the primary analysis (Table S22). Excluding participants who were lost to follow-up did not materially alter the observed associations (Table S23). Further adjustment for antihypertensive, lipid-lowering, and hypoglycemic medication use remained consistent with those observed in the primary analyses (Table S24). Moreover, subgroup analyses stratified by sociodemographic and lifestyle factors showed that the associations between IR-related indices and incident CMM were generally consistent across subgroups (Table S25). Formal interaction testing further revealed significant effect modification by sex and age for most IR-related indices (P for interaction < 0.05). Specifically, the associations tended to be stronger in women and in participants aged < 60 years (Table S25). In particular, similar associations were observed among participants who used anti-hypertensive medications and those who did not at baseline (Table S25), suggesting that anti-hypertensive treatment status did not materially modify the associations.
Discussion
In this observational prospective analysis of nearly 130,000 individuals with hypertension followed for over 13 years, we comprehensively assessed eight IR-related indices (TyG index, TyG-BMI, TyG-WC, TyG-WHtR, TyG-BRI, TyG-ABSI, TyG-WWI, and TG-HDL-C) in relation to incident CMM. Higher levels of all indices were associated with higher risks of CMM after adjustment for potential confounders, and each SD increase was associated with an 18%-48% higher risk. Results were consistent across sensitivity and subgroup analyses, and dose–response analyses suggested predominantly L-shaped associations. When added to traditional risk models, IR-related indices modestly improved CMM risk prediction, with TyG-WHtR contributing the largest gains. Importantly, we found evidence of both additive and multiplicative interactions between IR-related indices and genetic risk. Genetic risk appeared to strengthen these associations, and participants with both high IR burden and high genetic risk had the highest observed risk of incident CMM. Exploratory biomarker findings suggested that inflammatory, hepatic, and renal biomarker abnormalities may be relevant to these associations.
Hypertension remains a major global public health challenge, and its coexistence with IR may further increase the risk of CMDs [75]. Although previous studies have linked IR-related indices to CMM, most studies have focused on the general population [24–43], leaving the relevance of these indices to incident CMM in hypertensive individuals largely unclear. Our study comprehensively examined the associations between eight IR-related indices—encompassing both traditional (TyG index, TyG-BMI, TyG-WC, TyG-WHtR, and TG-HDL-C) and novel measures (TyG-BRI, TyG-ABSI, and TyG-WWI)—and incident CMM among individuals with hypertension. We found that all IR-related indices were positively associated with incident CMM, with TyG-WC and TyG-WHtR showing the strongest associations. Our findings are broadly consistent with previous studies conducted in hypertensive populations, which have demonstrated the prognostic value of IR-related indices for cardiovascular and mortality outcomes [17, 46, 47]. For example, a cohort study reported that traditional IR-related indices were associated with an increased risk of CVD among individuals with hypertension, with TyG-WC and TyG-WHtR exhibiting the strongest associations [17]. Similarly, another prospective cohort study reported that the TyG index and its derivatives, particularly the TyG-WHtR index, were closely associated with both all-cause and cardiovascular mortality in hypertensive individuals [46]. Unlike previous research that focused on single outcomes, our study extends the existing literature by examining CMM, capturing the accumulation of CMDs and providing a more comprehensive assessment of cardiometabolic burden in this high-risk population.
The magnitude of the observed associations also appears to be clinically relevant. Participants in the highest versus lowest tertiles of IR-related indices had a 73% to 129% higher risk of incident CMM. In a population consisting entirely of individuals with hypertension and a substantial long-term burden of incident CMM, these effect sizes suggest that these indices may provide useful additional information for risk stratification. Therefore, although IR-related indices, readily obtained from routine clinical measurements, may have potential value as practical adjunctive markers to identify hypertensive individuals at elevated risk, these findings should be interpreted cautiously until validated in independent cohorts. It is also important to interpret the definition of CMM in individuals with hypertension. In this setting, the occurrence of one additional CMD reflects the initial transition from a single condition to a multimorbid state, rather than the simultaneous presence of multiple diseases as commonly defined in general population studies. This distinction highlights different stages in the trajectory of disease progression—from the early transition to multimorbidity to the subsequent accumulation of additional CMDs. Notably, our findings remained consistent when a stricter definition of incident CMM was applied, suggesting that IR-related indices may capture metabolic risk processes that operate across different stages of disease accumulation in hypertensive individuals. Moreover, the observed associations were stronger in women and in participants aged < 60 years. Although women generally have more favorable insulin sensitivity and fat distribution than men, this advantage may diminish once IR worsens, hyperglycemia develops, or menopause occurs [76]. Therefore, once IR-related abnormalities arise, the corresponding increase in cardiovascular and metabolic risks may be more pronounced. The more pronounced associations in younger participants may be attributable to earlier and longer cumulative exposure to metabolic abnormalities, resulting in a greater lifetime burden of vascular and cardiometabolic injury. From a clinical perspective, our findings suggest that simple IR-related indices derived from routine clinical measurements, particularly TyG-WHtR, may serve as adjunctive markers for identifying hypertensive individuals at elevated risk of developing CMM, but additional validation is needed before they can be considered for broader risk prediction use beyond conventional factors.
Notably, previous studies based on hypertensive populations have largely focused on traditional IR-related indices [17, 46, 47]. Our study systematically compared the incremental predictive value of both traditional and emerging IR-related indices for CMM risk among individuals with hypertension. Our results showed that incorporating IR-related indices, particularly TyG-WHtR, into the traditional model was associated with improved predictive performance in this cohort. Supporting our findings, Tian et al. reported in a large cohort study that TyG-WHtR and TyG-WC showed significantly higher NRI and IDI values for predicting incident CMM in the general population [38]. Consistent findings have also been reported in other studies, including a cohort of 3885 middle-aged and older Chinese adults and a study of individuals with CKM syndrome, both showing that TyG-WHtR exhibited superior incremental predictive performance [33, 44]. These findings highlight the potential role of IR-related indices in the progression from hypertension to CMM. However, the incremental predictive analyses in the study were intended primarily to compare the relative predictive utility of different IR-related indices within the same population, rather than to establish a validated prediction model for direct clinical use. Therefore, these findings should be interpreted cautiously until validated in independent cohorts.
Our study extends previous research by integrating genetic risk with IR-related indices in the assessment of CMM risk among individuals with hypertension. We observed that individuals with both elevated IR-related indices and high genetic risk experienced the greatest risk of incident CMM, underscoring the importance of jointly considering these factors for CMM risk assessment. Consistent with our findings, a cohort study with a median follow-up of 11.9 years reported that individuals with both high HbA1c levels and high genetic risk had the highest risk of adverse cardiovascular events compared with those with low HbA1c levels and low genetic risk [77]. Additionally, we observed synergistic associations between IR-related indices and genetic risk in relation to CMM incidence, with statistically significant evidence of additive interaction. Approximately 13.4%-24.5% of CMM risk was attributable to the additive interactions, indicating that the joint effect of high genetic risk and high IR-related indices exceeded the sum of their individual effects. Supporting our findings, Hu et al. observed a significant additive interaction between TyG-WC and genetic risk on CVD incidence in a large cohort study [20]. Further stratified analyses showed that the adverse associations of several IR-related indices with CMM were more pronounced among individuals with high genetic risk, supported by statistically significant multiplicative interactions. Similar to our findings, Li et al. concluded in a large cohort study that genetic risk appeared to amplify the detrimental effects of unhealthy metabolic states on CVD [52]. The presence of significant additive and multiplicative interactions further suggests that IR may amplify the association of genetic risk with CMM. These findings underscore the importance of considering both inherited and modifiable metabolic factors when evaluating the risk of CMM in individuals with hypertension. From a research perspective, several important directions warrant further investigation. First, future studies should validate these findings in diverse populations and clinical settings. Second, mechanistic studies are needed to further elucidate the biological pathways linking IR, genetic susceptibility, and the risk of CMM. Third, prospective intervention studies should evaluate whether strategies aimed at improving IR, such as lifestyle modification or metabolic therapies, can reduce the risk of developing CMM among individuals with hypertension.
The biological mechanisms potentially underlying the associations between elevated IR-related indices and increased risk of CMM are likely complex and may involve multiple interrelated metabolic, inflammatory, and vascular processes. IR is widely recognized as an important pathophysiological feature in the development of CMDs and is closely associated with metabolic dysregulation, visceral adiposity, chronic inflammation, and multi-organ dysfunction [10]. In this study, we observed that biomarkers reflecting hepatic dysfunction, renal impairment, and chronic low-grade inflammation may be relevant to the associations between IR-related indices and incident CMM among individuals with hypertension. However, given the observational design of the present study and the fact that both IR-related indices and candidate biomarkers were measured at baseline, causal relationships cannot be established. Therefore, the biomarker analyses should be interpreted as exploratory and hypothesis-generating, aiming to identify biomarkers that may statistically account for part of the observed associations rather than to confirm specific causal mechanisms. Biomarkers such as liver enzymes and inflammatory markers may reflect components of broader metabolic dysfunction related to IR and could represent either potential intermediates or correlated indicators of underlying metabolic disturbances. First, hepatic dysfunction may represent an important correlated domain of dysfunction. In our analyses, biomarkers of liver function, particularly ALT and GGT, statistically accounted for approximately 3.5% to 10.2% of the associations. Previous studies have consistently reported positive associations between IR-related indices, particularly the TyG index, and liver enzyme levels [78, 79], and these biomarkers have also been linked to CMDs [80, 81]. Given the central role of the liver in glucose and lipid metabolism, hepatic IR may promote excessive lipid synthesis, de novo lipogenesis, and pro-inflammatory cytokine production, thereby leading to systemic metabolic disturbances, endothelial dysfunction, and accelerated atherosclerosis, ultimately increasing the risk of CMDs [82–84]. However, these interpretations should remain cautious, as liver-related biomarkers may also reflect broader metabolic dysfunction rather than discrete causal mediators. Second, renal dysfunction may also be a correlated process. Urate and cystatin C showed relatively prominent statistical contributions. Previous observational studies have reported that higher levels of IR-related indices are associated with impaired renal function and an increased risk of chronic kidney disease, particularly among individuals with hypertension [85–87]. Reduced kidney function has also been associated with a higher risk of adverse cardiovascular outcomes [81, 88, 89]. One possible explanation is that IR may contribute to renal injury through endothelial dysfunction, oxidative stress, inflammatory activation, and microvascular abnormalities [90, 91], while impaired kidney function may in turn exacerbate systemic metabolic imbalance, vascular stiffness, and systemic inflammation, thereby amplifying the risk of CMDs [92]. Third, chronic low-grade inflammation may represent another potentially relevant process underlying the associations. In our study, inflammatory biomarkers, including CRP, WBC, and neutrophil, count exhibited moderate but statistically significant contributions. IR has been linked to activation of pro-inflammatory signaling, sustained cytokine production, and oxidative stress [93, 94], which may adversely affect endothelial function and promote atherosclerotic progression [93, 94], ultimately potentially contributing to the development of multiple CMDs [81, 95]. Importantly, prior prospective studies have similarly suggested that dysregulation of inflammatory and hepatic and renal function-related biomarkers may be associated with the relationship between elevated IR-related indices and increased CMM or CVD risk [17, 20, 38]. Our findings are broadly consistent with these observations and extend them to individuals with hypertension. More broadly, hypertension and IR may interact through several overlapping processes. IR may worsen endothelial dysfunction, promote vascular inflammation, and accelerate atherosclerosis, all of which play critical roles in the development of CMM [96, 97]. Meanwhile, hypertension could induce hemodynamic stress, which results in left ventricular hypertrophy and vascular remodeling [98, 99]. Furthermore, IR may heighten the risk of adverse cardiovascular outcomes by disrupting endothelial integrity, fostering thrombosis, and enhancing platelet activation [100–102]. In the presence of hypertension, impaired renal vasorelaxation and reduced insulin sensitivity might further aggravate both IR and CMM progression [103]. The coexistence of IR and hypertension may therefore create a vicious cycle characterized by metabolic disturbance, vascular injury, and progressive multi-organ dysfunction, ultimately increasing the likelihood of developing CMM. Rather than establishing any single pathway, our results suggest that IR-related indices may reflect a broader network of metabolic and inflammatory disturbances involving the liver, kidney, and vascular systems that could contribute to progression from hypertension to CMM. These biomarker findings should be interpreted as exploratory and correlational, rather than as evidence of specific causal biological pathways. Future studies integrating multi-omics approaches and longitudinal biomarker data may help further evaluate these hypothesized biological mechanisms.
This study provides novel prospective insights into the associations of both conventional and emerging IR-related indices with incident CMM among individuals with hypertension. Major strengths include the large-scale cohort, prolonged follow-up, rigorous control of confounding, and extensive sensitivity and subgroup analyses. The inclusion of comprehensive biomarker data allowed for an in-depth investigation of potential biological mechanisms. Importantly, this is the first study to jointly examine genetic risk and IR-related indices to assess gene-IR interactions in relation to CMM incidence in a hypertensive population, offering potential value for precision prevention. However, several limitations should be noted. First, despite prior validation of UK Biobank-based association studies [104], the cohort is subject to healthy volunteer selection bias, as participants tend to be healthier, better educated, and of higher socioeconomic status than the general population [105]. Exclusion of participants with missing exposure data may have introduced additional bias, although baseline differences between included and excluded participants were modest (Table S26). The sequential inclusion and exclusion process may further exacerbate this bias, potentially limiting the generalizability of our findings to broader hypertensive populations and leading to underestimation of absolute disease risks. Second, as an observational study, our analyses remain susceptible to residual confounding despite adjustment for a wide range of sociodemographic, lifestyle, and clinical factors. Additionally, reverse causation cannot be fully excluded, although the associations remained materially unchanged after excluding CMM events within the first two or five years of follow-up. Third, these limitations are particularly relevant to the mediation analyses. Causal interpretation of mediation in an observational setting requires strong assumptions, including no unmeasured confounding of the exposure-outcome, exposure-mediator, and mediator-outcome relationships, as well as no mediator-outcome confounders affected by the exposure. These assumptions are not fully testable in the present study. Moreover, because both IR-related indices and candidate mediators were measured at the same time point, the temporal sequence between exposure and mediator could not be established. Therefore, the mediation findings should be interpreted as exploratory and hypothesis-generating, rather than as definitive evidence of causal biological pathways. Fourth, IR-related indices were assessed only at baseline, preventing evaluation of metabolic changes over time and potentially introducing exposure misclassification that may attenuate the observed associations. Future studies with repeated measurements of IR-related indices and biomarkers are needed to clarify their temporal sequence and to assess how changes in IR-related indices relate to CMM risk. Moreover, although cutoff-based classification may improve clinical interpretability and facilitate risk stratification, our study was designed to compare the relative incremental predictive performance of IR-related indices rather than to identify clinically meaningful thresholds, which would require dedicated threshold-identification analyses and external validation in independent populations. Also, the incremental predictive analyses were conducted within the same study population and were not subjected to external validation. Therefore, these findings should be interpreted as comparative and exploratory rather than as evidence of a validated prediction model, and further validation in independent cohorts is required before clinical application. Fifth, the predominantly White study population limits the generalizability of our findings to other ethnic groups and broader global populations. Therefore, our findings should be interpreted cautiously when extrapolated to non-White populations, and further validation in more ethnically diverse cohorts is warranted. Sixth, the cPRS was constructed using regression coefficients derived from the same study population without cross-validation or validation in an independent external cohort, which may introduce potential overfitting and lead to an overestimation of the association between genetic risk and CMM. Future studies incorporating external validation cohorts are warranted to further assess the robustness and generalizability of the genetic risk estimates. Seventh, some metabolic variables included in the fully adjusted model may be closely related to IR and could potentially lead to overadjustment. However, the associations were largely consistent between the model without metabolic adjustment and the fully adjusted model. Eighth, although the FDR correction was applied to both biomarker and main association analyses, the large number of statistical tests performed in this study still introduces the possibility of type I error. Finally, because all participants had hypertension at baseline, incident CMM in this study reflected progression from hypertension alone to hypertension with at least one additional CMD. Thus, outcomes such as CHD or stroke may also represent disease progression or complication rather than incident multimorbidity in the general population. Although similar associations were observed across subgroups defined by baseline anti-hypertensive treatment use, this issue should still be considered when interpreting our findings.
Conclusions
In this large observational prospective cohort analysis of individuals with hypertension, eight IR-related indices were positively associated with incident CMM, with TyG-WHtR showing the strongest predictive performance. Significant additive and multiplicative interactions with genetic risk were observed, indicating that genetic susceptibility may strengthen the associations between IR-related indices and CMM risk. Biomarkers reflecting inflammation and hepatic and renal dysfunction were statistically related to the observed associations. Overall, these findings suggest that IR-related indices, particularly TyG-WHtR, may provide useful information for CMM risk stratification among individuals with hypertension.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We thank all participants and staff from the UK Biobank study. The current study was conducted using the UK Biobank data under Application Number 68136.
Abbreviations
- CMM
Cardiometabolic multimorbidity
- CMDs
Cardiometabolic diseases
- CVD
Cardiovascular disease
- CHD
Coronary heart disease
- IR
Insulin resistance
- TyG
Triglyceride-glucose
- BMI
Body mass index
- WC
Waist circumference
- WHtR
Waist-to-height ratio
- BRI
Body roundness index
- ABSI
A body shape index
- WWI
Weight-adjusted waist index
- TG
Triglyceride
- HDL-C
High-density lipoprotein cholesterol
- ICD-10
International Classification of Diseases, Tenth Revision
- SBP
Systolic blood pressure
- DBP
Diastolic blood pressure
- PRSs
Polygenic risk scores
- HbA1c
Glycated hemoglobin
- T1
Tertile 1
- CRP
C-reactive protein
- WBC
White blood cell count
- ALP
Alkaline phosphatase
- AST
Aspartate aminotransferase
- GGT
Gamma-glutamyltransferase
- TP
Total protein
- DB
Direct bilirubin
- LDL-C
Low-density lipoprotein cholesterol
- TDI
Townsend Deprivation Index
- SD
Standard deviation
- HR
Hazard ratio
- CI
Confidence interval
- IQR
Interquartile range
- RCS
Restricted cubic spline
- KM
Kaplan–Meier
- NRI
Net reclassification index
- IDI
Integrated discrimination improvement index
- C-index
Concordance index
- RERI
Relative excess risk due to interaction
- AP
Attributable proportion due to interaction
- SI
Synergy index
- PM
Proportion mediated
- FDR
False discovery rate
Author contributions
Shaowei Ma, Yueping Shen, Kuanbing Chen, and Bin Li conceived and designed the research, Yueping Shen performed the data analysis, Shaowei Ma and Ying Xuan wrote the manuscript, Shaowei Ma, Ying Xuan, Yueping Shen, and Min Jiang interpreted the analyzed results, and Shaowei Ma, Ying Xuan, Min Jiang, Yueping Shen, Kuanbing Chen, and Bin Li revised the manuscript critically for important intellectual content. All authors contributed to the interpretations of the findings and reviewed the manuscript. All authors read and approved the final manuscript.
Funding
This research received no external funding.
Data availability
The data that support the findings of this study are available from the UK Biobank team (https://www.ukbiobank.ac.uk/) upon application. The methods and corresponding Codes that support the findings of this study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The Northwest Multi-center Research Ethics Committee (MREC reference: 21/NW/0157) provided ethical approval for the UK Biobank project. All participants gave informed consent before being recruited.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Shaowei Ma, Min Jiang and Ying Xuan contributed equally to this work.
Contributor Information
Yueping Shen, Email: shenyueping@suda.edu.cn.
Kuanbing Chen, Email: chenkb23@163.com.
Bin Li, Email: Libin2419@hotmail.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are available from the UK Biobank team (https://www.ukbiobank.ac.uk/) upon application. The methods and corresponding Codes that support the findings of this study are available from the corresponding author on reasonable request.