Abstract
Background
Hypertension-induced left ventricular hypertrophy (LVH) is intricately linked to insulin resistance (IR). This research aimed to elucidate the relationship of advanced indices, namely the triglyceride–glucose (TyG) index, the TyG adjusted for body mass index (TyG-BMI), the triglycerides-to-high-density lipoprotein cholesterol ratio (TG/HDL-c), and the metabolic score for IR (METS-IR), with LVH in hypertensive cohorts.
Methods
This analytical case–control investigation encompassed 800 individuals aged 18 or above from the Cardiology Department of the Second Affiliated Hospital of Baotou Medical College over a span from January 2021 to April 2022. Data extraction was conducted from inpatient records. The nexus between the four metrics and LVH susceptibility was ascertained via logistic regression models. Furthermore, the receiver operating characteristic (ROC) curve’s area (AUC) shed light on the discriminative capacities of the distinct IR indicators for LVH, considering other concomitant risk variables.
Results
Post multifaceted covariate adjustments, the fourth quartile figures for TyG-BMI emerged as the most starkly significant (OR: 5.211, 95% CI: 2.861–9.492), succeeded by METS-IR (OR: 4.877, 95% CI: 2.693–8.835). In juxtaposition with other IR-derived indices (TyG and TG/HDL-c), TyG-BMI manifested the paramount AUC (AUC: 0.657; 95% CI: 0.606–0.708). Concurrently, METS-IR exhibited commendable predictive efficacy for LVH (AUC: 0.646; 95% CI: 0.595–0.697).
Conclusion
TyG-BMI and METS-IR displayed superior discriminative capabilities for LVH, underscoring their potential as supplementary indicators in gauging LVH peril in clinical settings and prospective epidemiological research.
Introduction
Left ventricular hypertrophy (LVH), a manifestation of cardiac remodeling, is a significant consequence of hypertension (1, 2, 3). It stands as a critical predictor of morbidity and mortality among individuals with hypertension, elevating the risk for several cardiovascular complications like coronary heart disease, sudden death, heart failure, atrial fibrillation, and stroke (4). Consequently, early detection of populations at heightened risk of developing LVH is crucial to mitigate associated disabilities and fatalities (5).
Insulin resistance (IR) is a central factor in cardiovascular diseases (6). In hypertensive patients, IR not only exacerbates hypertension but also contributes to hypertension-mediated organ damage, such as LVH and microalbuminuria (7, 8, 9). IR is implicated in the onset and progression of metabolic cardiomyopathy, as evidenced by experimental studies demonstrating its negative impact on cellular metabolism and signaling, which, in turn, affects left ventricular contractility and stiffness (7, 10, 11, 12, 13). This suggests that assessing IR could be instrumental in identifying individuals at elevated risk for LVH.
The hyperinsulinemic–euglycemic clamp test (HEC) is the gold standard for measuring insulin sensitivity (14, 15). However, its complexity, cost, invasiveness, and requirement for skilled personnel render it impractical for routine clinical use or large-scale epidemiological studies. Consequently, there is a need for simpler, more feasible surrogate markers of insulin sensitivity. Previous epidemiological studies have utilized such non-insulin-based fasting IR indicators, including the triglyceride–glucose (TyG) index (16, 17), the TyG index adjusted for body mass index (TyG-BMI) (18, 19), the triglycerides/high-density lipoprotein cholesterol ratio (TG/HDL-c) (20, 21), and the metabolic score for IR (METS-IR) (22). These surrogates offer an accessible means to evaluate individual IR levels and, by extension, potential risks for LVH in a large-scale, clinically practical context.
Methods
Study population
This retrospective study involved 603 hypertensive patients admitted to the Second Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology (This study was approved by the Medical Ethics Committee of the Second Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology). Spanning from January 2021 to April 2022, the cohort consisted of 313 males and 290 females, averaging 64 ± 9.8 years of age. These individuals were all diagnosed according to the criteria set in the 2018 Chinese Guidelines for the Prevention and Treatment of Hypertension. Left ventricular hypertrophy (LVH) within this group was identified by a left ventricular mass index (LVMI) cutoff point of ≥115 g/m² for males and ≥95 g/m² for females. The study divided the participants into two distinct groups based on the presence of LVH: the LVH group with 152 patients and the non-LVH group containing 451 patients. Figure 1 provides a visual depiction of the participant selection process.
Inclusion and exclusion criteria
The researchers established clear criteria for participation in the study. The inclusion criteria allowed for individuals aged 18 years or older who had been previously diagnosed with hypertension. On the other hand, several exclusion criteria were set to narrow down the subject pool to the most suitable candidates for this particular investigation: (i) secondary hypertension; (ii) conditions such as hypertrophic cardiomyopathy, severe arrhythmias (including atrial fibrillation, atrial flutter, ventricular tachycardia, etc.), heart valve disease, New York Heart Association (NYHA) class III or IV; (iii) acute cerebrovascular disease; (iv) significant liver or kidney dysfunction; (v) hematologic or rheumatic immune system diseases and the use of hormone or immunosuppressive drugs; (vi) hyperthyroidism or hypothyroidism; (vii) presence of malignant tumors.
This structured inclusion and exclusion process aimed to create a homogeneous study population, thereby increasing the validity and reliability of the findings regarding the relationship between insulin resistance and the risk of developing LVH in a hypertensive cohort.
Data collection
All participants provided written informed consent. Participant information included age, sex, height, weight, smoking status, drinking status, physical activity, medical history (diabetes mellitus, dyslipidemia, myocardial infarction, stroke, etc.), and laboratory markers (total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), and fasting glucose (FPG)). Height and weight were measured with participants in light clothing, standing without shoes. Two readings were taken for accuracy and the mean value recorded. RHR and blood pressure were measured twice using an automated sphygmomanometer (Omron HEM-7125, Kyoto, Japan) after subjects rested in a sitting position for at least 5 min. After ensuring an 8-h fasting period for the participants, blood samples were collected to determine the level of FPG, TC, TG, HDL-c, and LDL-C. All biochemical indices were determined by standard experimental methods.
Study definitions
Body mass index (BMI) was ascertained through the quotient of body mass in kilograms by the square of stature in meters (kg/m2). An individual was characterized as a current smoker if they reported consumption of no less than one cigarette daily at the juncture of the assessment. Alcohol intake was delineated as the consumption of alcoholic beverages at a minimum frequency of once per diem. Hypertension was stratified into three categories predicated upon the apogee of antecedent blood pressure readings: class I (systolic blood pressure (SBP) 140–159 or diastolic blood pressure (DBP) 90–99 mm Hg), class II (SBP 160–179 or DBP 100–109 mm Hg), and class III (SBP ≥180 or DBP ≥110 mm Hg) (23).
Participants of the study were subjected to echocardiographic examination utilizing the Siemens VividS70N color Doppler ultrasound system (probes: M5Sc) to ascertain the presence of left ventricular hypertrophy (LVH). Imagery was procured in the left lateral decubitus position amid tranquil respiration, employing transthoracic echocardiography as the gold standard for left ventricular mass (LVM) approximation. Measurements such as interventricular septal diastolic thickness (IVSD), left ventricular end-diastolic diameter (LVEDD), and posterior wall thickness (PWD) were meticulously recorded from the parasternal long-axis perspective. LVM was computed adhering to the American Society of Echocardiography's guidelines as LVM (g) = 0.8 × 1.04 × ((IVSD + LVEDD + PWD)3 − LVEDD3) + 0.6, and subsequently indexed to body surface area, yielding the left ventricular mass index (LVMI). Under the updated criteria by the same society, LVMI exceeding 95 g/m2 in females and 115 g/m2 in males was indicative of LVH (24).
The focal point of exposure encompassed four surrogate indices of insulin resistance (IR), specifically the triglyceride–glucose index (TyG), TyG-BMI, the triglyceride-to-high-density lipoprotein cholesterol ratio (TG/HDL-c), and the metabolic score for insulin resistance (METS-IR), each derived from the subsequent formulas:
TyG = ln (TG (mg/dL) × FPG (mg/dL)/2) (19)
TyG-BMI = TyG × BMI (19)
TG/HDL-c = TG (mmol/L)/HDL-c (mmol/L) (21)
METS-IR = ln ((2 × FPG (mg/dL)) + TG (mg/dL)) × BMI)/(ln (HDL-c (mg/dL))) (22)
Statistical analysis
Continuous variables were presented as mean values with their corresponding standard deviations. Categorical variables were reported as counts and percentages. The chi-square test for categorical variables and the Kruskal–Wallis test for continuous variables were used to compare the mean levels of variables between subjects with and without LVH. Multiple logistic regression models were constructed to evaluate the relationship between four insulin resistance-related indices and the risk of LVH. These indices were categorized into quartiles and considered as categorical variables in the regression models. Three models of increasing complexity were specified: model 1 (Crude), no adjustments were made, providing a basic measure of association; model 2, adjusted for confounding variables including age, sex, education level, smoking, and drinking habits; model 3, adjusted for all variables in model 2 plus marital status, hypertension classification, diabetes mellitus, hyperlipidemia, coronary heart disease, stroke, myocardial infarction, stent implantation, cardiac insufficiency, and EF (ejection fraction). Restricted cubic splines were used within the logistic regression models to explore and test for any nonlinear relationships between the insulin resistance indices and LVH risk. The diagnostic potential of the IR-related indices (TyG, TyG-BMI, TG/HDL-c, and METS-IR) for identifying LVH was assessed using ROC curves. The area under the ROC curve (AUC) was calculated to measure the accuracy of these indices as screening tools for LVH. Statistical analyses were performed by using SPSS® 25.0 software and RStudio (R4.2.1) software were used for statistical analysis and graphing of data. P < 0.05 with two-sided tests was considered to indicate statistical significance.
Results
Characteristics of the study participants
Table 1 summarizes the sociodemographic features, smoking and alcohol consumption habits, physical activity levels, along with the results of physical examinations and laboratory tests for the study cohort. This observational study encompassed 603 participants. Among them, the median age was 64.00 years (interquartile range: 57.00–71.00 years) for individuals without left ventricular hypertrophy (non-LVH) and 67.00 years (interquartile range: 60.00–73.00 years) for those with LVH. Echocardiography identified 152 participants (25.4%) with LVH. Notably, the LVH group had a higher proportion of female participants compared to the non-LVH group.
Baseline information of the overall population.
| N-LVH (n = 451) | LVH (n = 152) | χ2/z | P | |
|---|---|---|---|---|
| Female (n, %) | 208 (46.12) | 82 (53.95) | 2.79 | 0.095 |
| Education (n, %) | 7.98 | 0.092 | ||
| Illiterate | 41 (9.09) | 17 (11.18) | ||
| Primary education | 115 (25.50) | 51 (33.55) | ||
| Middle school education | 136 (30.16) | 48 (31.58) | ||
| High school education | 106 (23.50) | 24 (15.79) | ||
| Bachelor degree or above | 53 (11.75) | 12 (7.89) | ||
| Smoking, former or current smokers (n,%) | 121 (26.83) | 56 (36.84) | 5.50 | 0.019 |
| Drinking (n, %) | 87 (19.29) | 41 (26.97) | 4.01 | 0.045 |
| Blood pressure classification (n, %) | 3.18 | 0.204 | ||
| Class I | 36 (8.04) | 10 (6.58) | ||
| Class II | 151 (33.71) | 41 (26.97) | ||
| Class III | 261 (58.26) | 101 (66.45) | ||
| Diabetes (n, %) | 117 (25.94) | 52 (34.21) | 5.14 | 0.023 |
| Hyperlipidemia (n, %) | 46 (10.20) | 25 (16.45) | 4.27 | 0.039 |
| History of CVD (n, %) | 176 (39.02) | 74 (48.68) | 4.37 | 0.037 |
| Stroke (n, %) | 142 (31.49) | 54 (35.53) | 0.85 | 0.351 |
| Myocardial infarction (n, %) | 37 (8.20) | 21 (13.82) | 2.49 | 0.115 |
| Stent implantation (n, %) | 64 (14.19) | 22 (14.47) | 0.01 | 0.931 |
| Heart failure (n, %) | 25 (5.54) | 15 (9.87) | 3.43 | 0.064 |
| Age (years) | 64.00 (57.00; 71.00) | 67.00 (60.00; 73.00) | −2.71 | 0.007 |
| Height (m) | 168.00 (160.00; 173.00) | 165.00 (160.00; 170.00) | −3.71 | <0.001 |
| Weight (kg) | 70.00 (60.50; 75.00) | 70.00 (65.00; 79.00) | −2.00 | 0.046 |
| Heart rate | 77.00 (71.00; 85.00) | 76.00 (68.00; 84.25) | −1.33 | 0.183 |
| BMI (kg/m2) | 24.69 (22.53; 26.97) | 25.93 (24.22; 28.58) | −4.30 | <0.001 |
| EF (%) | 63.00 (60.00; 66.00) | 62.00 (58.75; 64.00) | −3.74 | <0.001 |
| TC (mg/dL) | 4.18 (3.47; 4.92) | 4.28 (3.70; 5.18) | −2.29 | 0.022 |
| TG (mg/dL) | 1.48 (1.09; 1.92) | 1.67 (1.25; 2.20) | −3.39 | 0.001 |
| HDL-C (mg/dL) | 1.10 (0.94; 1.30) | 1.08 (0.92; 1.31) | −0.13 | 0.895 |
| LDL-C (mg/dl) | 2.70 (2.00; 3.23) | 2.76 (2.27; 3.61) | −2.71 | 0.007 |
| FPG (mg/dL) | 5.30 (4.80; 5.95) | 5.50 (4.90; 6.43) | −2.66 | 0.008 |
| TyG | 8.76 (8.42; 9.09) | 8.91 (8.62; 9.53) | −4.07 | <0.001 |
| TyG-BMI | 217.01 (195.92; 238.95) | 237.94 (209.20; 260.34) | −5.78 | <0.001 |
| TG/HDL-C | 3.00 (2.13; 4.35) | 3.51 (2.46; 5.30) | −3.33 | 0.001 |
| METS-IR | 38.45 (33.92; 42.39) | 41.89 (37.52; 46.04) | −5.39 | <0.001 |
BMI, body mass index; EF, ejection fraction; FPG, fasting glucose; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; LVH, Left ventricular hypertrophy; METS-IR, metabolic score for insulin resistance; TC, total cholesterol; TG, total triglyceride; TG/HDL-C, the ratio of triglycerides divided by high-density lipoprotein cholesterol; TyG, triglyceride glucose; TyG-BMI, triglyceride glucose with body mass index.
Individuals with LVH exhibited several distinct characteristics, including advanced age, elevated body mass index (BMI), fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), TyG index (a marker of insulin resistance), TyG-BMI index, TG/HDL-C ratio, and METS-IR values. Conversely, high-density lipoprotein cholesterol (HDL-C) levels were lower in the LVH group (P < 0.05). Furthermore, the prevalence of hypertension, diabetes, and cardiovascular diseases (CVDs) was notably higher among participants with LVH. It is worth noting that there were significant disparities in lifestyle choices, with LVH patients exhibiting a higher tendency to engage in smoking and alcohol consumption.
Association between insulin resistance surrogates and LVH
Figure 2 illustrates the relationship between LVH and surrogate markers of insulin resistance (IR) when analyzed as categorical variables. The results depicted in Fig. 2 demonstrate a significant positive correlation between insulin resistance surrogates and LVH, both before and after adjusting for various confounding factors. The risk of developing LVH exhibited a noteworthy increase with escalating quartiles of TyG, TyG-BMI, TG/HDL-C, and METS-IR. Notably, LVH displayed a robust correlation with parameters indicative of insulin resistance, with TyG-BMI exhibiting the most robust association with LVH.
Odds ratios for LVH in the different quartiles of the four IR surrogates in the three models.
Citation: Endocrine Connections 13, 4; 10.1530/EC-23-0476
Upon comparing odds ratios (ORs) of these factors, it became evident that the impact of being in the highest quartile of TyG-BMI was particularly pronounced after adjustment in model 2 (OR: 5.211, 95% CI: 2.861–9.492). Following closely, METS-IR also displayed a substantial positive effect on LVH risk after adjustment (OR: 4.877, 95% CI: 2.693–8.835). Additionally, TyG (OR: 2.413, 95% CI: 1.377–4.299) and TG/HDL-C (OR: 2.651, 95% CI: 1.481–4.744) demonstrated significant associations with LVH, albeit to a slightly lesser extent compared to TyG-BMI and METS-IR. These findings underscore the strong connection between insulin resistance indices and the presence of LVH, with TyG-BMI exhibiting the most prominent association in this context.
Restricted cubic spline curves for insulin resistance surrogates and LVH risk
Figure 3 shows the dose–response relationships between the above factors and LVH risk by multivariable adjusted restricted cubic spline analysis after adjusting age, sex, education, smoking, drinking, hypertension classification, diabetes mellitus, hyperlipidemia, coronary heart disease, stroke, myocardial infarction, stent implantation, cardiac insufficiency, and EF. The analysis confirmed that there is a nonlinear positive correlation between the risk of LVH and the four IR-related indices: TyG, TyG-BMI, TG/HDL-c, and METS-IR. This means that as the value of these indices increases, the risk of LVH also increases, but not in a straight-line (linear) fashion. Instead, the rate of increase in risk may change at different levels of these indices. Among the indices, TyG-BMI was found to have the largest effect on LVH risk, indicating that it may be the most potent predictor among those analyzed. METS-IR followed as the second most significant predictor.
Relationship of TyG, TyG-BMI, TG/HDL-C, and METS-IR with the risk of LVH for all participants.
Citation: Endocrine Connections 13, 4; 10.1530/EC-23-0476
Accuracy of insulin resistance surrogates in identifying LVH
Figure 4 and Table 2 show the results of ROC analysis and the AUCs of four IR-related indices used to predict LVH. TyG-BMI and METS-IR had a good predictive value for the prevalence of hypertension. The predictive value of TyG-BMI for the prevalence of hypertension was mildly preferable to METS-IR. The AUC of TyG-BMI parameters was the largest, which was 0.657 (AUC: 0.657;95% CI: 0.606–0.708), and the cutoff value was 239.98, with a sensitivity and specificity of 49.34% and 75.80%, respectively. Whereas, the area under the METS-IR curve (AUC) was 0.646 (95% CI: 0.595–0.697) and the cutoff value was 42.65, with a sensitivity and specificity of 48.03% and 76.5%, respectively. Despite attempts to combine TyG-BMI, TyG, TG/HDL, and METS-IR to further predict the prevalence of LVH, the predictive value did not improve.
The receiver operating characteristic curve of insulin resistance surrogates and LVH after adjusting for age, sex, education, smoking, drinking, hypertension classification, diabetes mellitus, hyperlipidemia, coronary heart disease, stroke, myocardial infarction, stent implantation, cardiac insufficiency, and EF.
Citation: Endocrine Connections 13, 4; 10.1530/EC-23-0476
Predictive value of four insulin resistance surrogates for the prevalence of LVH.
| Variables | AUC (95% CI) | Cutoff | Sensitivity | Specificity | P |
|---|---|---|---|---|---|
| TYG | 0.610 (0.558, 0.663) | 9.51 | 26.32% | 91.13% | <0.001 |
| TYG-BMI | 0.657 (0.606, 0.708) | 239.98 | 49.34% | 75.80% | <0.001 |
| TG/HDL | 0.590 (0.537, 0.643) | 6.335 | 21.71% | 92.24% | 0.001 |
| METS-IR | 0.646 (0.595, 0.697) | 42.65 | 48.03% | 76.50% | <0.001 |
| TYG+TYG-BMI+TG/HDL+METS-IR | 0.667 (0.616, 0.717) | 0.3236 | 42.76% | 83.15% | <0.001 |
AUC, area under the curve; LVH, left ventricular hypertrophy; METS-IR, metabolic score for insulin resistance; TG/HDL-C, the ratio of triglycerides divided by high-density lipoprotein cholesterol; TyG, triglyceride–glucose; TyG-BMI, triglyceride–glucose with body mass index.
Discussion
In our research, we employed multivariate logistic regression to scrutinize the links between insulin resistance (IR) proxies and the incidence of left ventricular hypertrophy (LVH) in individuals suffering from hypertension. Our analysis revealed a notable elevation in TyG index, TyG-BMI, triglyceride-to-HDL cholesterol ratio (TG/HDL-c), and METS-IR, each presenting a heightened likelihood of LVH manifestation. The dose–response relationship that was analyzed by using restricted cubic splines demonstrated a nonlinear relationship between four IR surrogates and LVH among all of the participants. The main findings were summarized as follows: (1) the TyG index, TyG-BMI, TG/HDL-c, and METS-IR demonstrated a significant uptick within the LVH group when juxtaposed with counterparts devoid of LVH; (2) four IR surrogates were independently associated with the prevalence of LVH, with TyG-BMI and METS-IR having the strongest association with LVH; (3) there were nonlinear positive association and dose–response relationship between four IR surrogates and LVH risk; and (4) TyG-BMI and METS-IR had good predictive value for the prevalence of LVH, and TyG-BMI was superior to METS-IR.
IR is characterized by a diminished response of peripheral tissues to the action of insulin (25). It has been linked with an array of cardiovascular risk factors, suggesting it could be a central player in the pathophysiology of cardiovascular disease (26). Studies suggest that IR may contribute to the development of LVH independently of blood pressure (BP) and body mass index (BMI) (27). Experimental evidence indicates that IR leads to cellular metabolic and signaling disturbances that affect LV contractility and stiffness (10, 11, 12). Insulin resistance can suppress the expression of glucose transporter type 4 (GLUT4), compromising the myocardial capacity for efficient energy utilization and metabolic flexibility, shifting from fatty acid to glucose metabolism under stress conditions (10, 28). IR can lead to oxidative stress and inflammation, which further contributes to the development of both microangiopathy and macroangiopathy – conditions that impair blood vessel function and can contribute to the progression of LVH (29). The influx of fatty acids into the mitochondria, which is increased in the setting of IR, can lead to an overproduction of reactive oxygen species such as superoxide ions, which may have been implicated in the pathogenesis of myocardial hypertrophy, fibrosis, and LV dysfunction (30). The surrogates for IR in our study – TyG index, TyG-BMI, TG/HDL-c, and METS-IR – are nontraditional lipid indices that can be calculated from routine blood tests. They are gaining attention because they not only provide insights into lipid metabolism abnormalities (31), but also reflect oxidative stress levels (32) and cardiovascular risk better than traditional lipid measures (33, 34, 35), and predict the left ventricular configuration (36). Moreover, abnormal lipid metabolism may lead to the accumulation of cardiac fat, thereby promoting LVH. One autoptic study of human hearts indicated that the fat deposition in the left ventricle constitutes a direct risk of cardiac hypertrophy (37). Abnormal lipid metabolism was usually accompanied by insulin resistance in the animal model of high-fat Feeding (38) and promoted LVH (39). This may be related to the CD40L pathway (40). The disruption of lipid metabolism activates the CD40/CD40L pathway, inducing the production of several potent proinflammatory cytokines (41), which in turn trigger the genes involved in cardiac inflammation and hypertrophy (42). The activation of the CD40L pathway also promotes cellular lipid uptake, altering the function and expression of sensitive KATP channels in the myocardium (43), causing cardiac hypertrophy. Moreover, dyslipidemia can promote the occurrence of left ventricular hypertrophy by inducing the activation of the ERK/MAPK pathway (44), increasing mRNA expression of AT2 receptors (45), and activating the sympathetic nervous system (46). Therefore, it may be reasonable that IR surrogates have potential predictive value for LVH.
Several previous studies have investigated the relationship between IR surrogates and hypertension. The study by Ning et al. (47) indicates that gender differences exist in the impact of IR on LVH incidence, with triglycerides-to-HDL cholesterol (TG/HDL-C) ratio being a significant predictor of LVH. This might reflect underlying differences in body fat distribution, hormonal influences, or lipid metabolism between men and women, which could affect the heart's structure and function. The triglyceride–glucose (TyG) index, as noted by Liu et al. (48), is highlighted as an important factor in the development of LVH among hypertensive patients. An elevated TyG indicates a higher risk for LVH, suggesting that this simple calculation may be useful in clinical settings to identify patients who need more.
In contrast to the above, our study showed that TyG-BMI and METS-IR were the best discriminator for LVH than all other IR-related parameters, including TyG, and TG/HDL-c. The superiority of TyG-BMI and METS-IR was consistent across various IR-related diseases. In a cross-sectional study focusing on the association between different IR surrogates and with nonalcoholic fatty liver, TyG-BMI and METS-IR demonstrated the strongest association with nonalcoholic fatty liver disease, along with the best predictive efficacy (49). Furthermore, a cross-sectional study conducted by the Rich Healthcare Group in China, involving 117,056 participants, revealed that TyG-BMI and METS-IR outperformed other parameters in predicting hypertension (50). This aligns with the findings of Wang et al. (51), who reported that the association between TyG-BMI and METS-IR exhibited a superior ability to identify hyperuricemia compared to other IR surrogates, as analyzed from data obtained from the National Health and Nutrition Examination Survey (NHANES). The mechanism behind the enhanced predictive ability of TyG-BMI and METS-IR index compared to TyG and TG/HDL-c is not fully understood. This may be attributed to the fact that TyG-BMI and METS-IR encompass not only abnormal glucose metabolism and defective fatty acid metabolism, as seen in TyG and TG/HDL-c, but also incorporate BMI as one of the obesity indices. This addition of BMI to the indices may contribute to an improved diagnostic capability. Including BMI in these indices may offer a more comprehensive representation of metabolic health by accounting for the influence of obesity. Obesity is known to be closely linked to insulin resistance, abnormal glucose metabolism, and other metabolic disturbances. Therefore, combining BMI with markers related to glucose and lipid metabolism in TyG-BMI and METS-IR may provide a more holistic assessment of metabolic health, potentially enhancing their predictive power.
Limitations
First, due to the retrospective nature of the study, it is challenging to establish a causal relationship between IR surrogates and LVH risk, meaning the findings can indicate correlation but not causation. The causal relationship between risk factors still needs to be further verified by longitudinal follow-up studies. Secondly, instead of the hyperinsulinemic–euglycemic glucose clamp method, IR was measured using surrogate biomarkers. Although the validity of these biomarkers has been reviewed in earlier research, there may be a misclassification of the potential influence of IR on the incidence of LVH. Finally, with the study population primarily being older individuals from Baotou, the findings may not be universally applicable to other ethnic groups or age demographics.
Conclusion
TyG-BMI and METS-IR were independent risk factors for the prevalence of hypertension. TyG-BMI and METS-IR had pronounced discrimination ability to LVH, which are recommended as complementary markers for the assessment of LVH risk both in clinic and in future epidemiological studies.
Disclosure of interest
The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of this study.
Funding
This study was supported by the Baotou Health and Wellness Committee (fund no. Wsjkwkj010).
Author contribution statement
Yue Jiamwei: Conceptualization, Supervision, Project administration; Wang Zichao: Methodology, Validation; Zhai Yumei: Writing – Original Draft, Investigation, Resources; Fu Haiming: Resources, Data Curation. Li Yu: Writing – Review and Editing, Investigation; LI Siyuan: Visualization, Writing – Review and Editing; Zhang Wenchen: Data Curation, Resources.
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