Abstract
Background
Cholesterol, high-density lipoprotein, and glucose (CHG) index, an alternative marker of insulin resistance (IR), play a significant role in predicting cardiovascular diseases. However, its prognostic value in patients with calcific aortic valve stenosis (CAVS) remains unclear.
Methods
This study included 1175 patients diagnosed with calcific aortic valve stenosis via echocardiography from the First Affiliated Hospital of Sun Yat-sen University. Participants were grouped based on the cut-off value of the CHG index. The association between the CHG index and cardiovascular mortality and all-cause mortality in patients with calcific aortic valve stenosis was evaluated using Cox proportional hazards regression and restricted cubic model spline.
Results
Among the 1175 patients (mean age 68.91 ± 11.68 years, 56.6% male), the median follow-up time was 3.23 [1.15, 6.07] years. In the fully adjusted model, each 1-unit increase in the CHG index was linked to a 53% higher risk of cardiovascular mortality and a 43% higher risk of all—cause mortality. Moreover, compared to the low CHG index group, the high CHG index group had a 1.44—fold higher risk of cardiovascular mortality and a 1.43-fold higher risk of all-cause mortality. The restricted cubic spline model indicated a linear relationship between the CHG index and the risks of cardiovascular mortality (p for nonlinearity = 0.529) and all-cause mortality (p for nonlinearity = 0.436).
Conclusion
Higher levels of insulin resistance, as assessed by the CHG index, are associated with increased risks of cardiovascular and all-cause mortality in patients with calcific aortic valve stenosis.
Trial registration
RISk facTOr assessmenT and prognosis modeL construction (ARISTOTLE) study (Registry: ClinicalTrials.gov, TRN: NCT06069232, Registration date: 1 October 2023).
Graphical abstract
Supplementary Information
The online version contains supplementary material available at 10.1186/s12933-025-02906-2.
Keywords: Cholesterol, High-density lipoprotein, And glucose index, Calcific aortic valve stenosis, Cardiovascular mortality, All-cause mortality
Research insights
What is currently known about this topic?
CAVS prevalence increases with age and is linked to higher cardiovascular mortality.
Insulin resistance significantly impacts aortic stenosis progression and mortality.
Traditional IR markers like TyG index predict CVD risk but have limitations.
What is the key research question?
Is the CHG index associated with cardiovascular and all—cause mortality in patients with calcific aortic valve stenosis?
What is new?
First study linking CHG index to CAVS mortality risks.
CHG index shows superior predictive ability for CVD risk in CAVS patients.
Reveals potential biological mechanisms of insulin resistance in CAVS progression.
How might this study influence clinical practice?
CHG index may serve as a tool for early identification of high—risk CAVS patients and guide clinical monitoring and treatment decisions.
Introduction
Calcific aortic valve stenosis, a common degenerative valve disease predominantly seen in the elderly, has an increasing prevalence with age, reaching 4% in individuals aged 85 and older [1, 2]. Consequently, cardiovascular deaths attributed to aortic stenosis are on the rise annually [3, 4]. Once the disease progresses to a symptomatic stage, the 2-year survival rate under conservative treatment drops below 50%, and the 5-year survival rate falls below 20% [1]. Currently, no evidence supports that medical therapy can slow disease progression. Aortic valve replacement (AVR) stands as the sole treatment option. Yet, a significant number of patients fail to receive AVR in time due to various reasons [5, 6]. The ACC/AHA and ESC/EACTS Guidelines emphasize that early identification and intervention of risk factors influencing aortic stenosis are crucial for disease prevention and prognosis improvement [7].
Hyperglycaemia and insulin resistance have emerged as LDL-C-independent drivers of CAVS. The Swedish CArdioPulmonary bioImage Study (SCAPIS) revealed that individuals with pre-diabetes or newly diagnosed diabetes exhibit a markedly higher prevalence of aortic valve calcification (AVC), demonstrating that even the earliest stages of dysglycaemia are sufficient to initiate valvular calcification [8]. Subsequently, Kopytek et al. elucidated that poorly controlled diabetes promotes accumulation of advanced glycation end-products (AGEs) and their receptor (RAGE) within the valve, thereby accelerating disease progression [9]. Reinforcing these findings, the nationwide AMORIS cohort—comprising 320 000 Swedish adults—demonstrated a dose–response relationship between “glucose imbalance” (spanning impaired fasting glucose, hyperglycaemia and diabetes) and incident CAVS. Compared with normoglycaemic individuals, the risk of CAVS rose by 36% in those with impaired fasting glucose (6.1–6.9 mmol/L), 79% in those with hyperglycaemia (≥ 7.0 mmol/L) and 121% in those with overt diabetes [10]. Collectively, these studies implicate IR as the pivotal mechanism linking dysglycaemia to CAVS; however, the predictive utility of conventional IR indices (HOMA-IR, TyG index) in valvular disease remains insufficiently explored.
Previous studies indicate that insulin resistance (IR) significantly impacts the progression of aortic stenosis and mortality events [11, 12]. While the hyperinsulinemic-euglycemic clamp (HEC) is the gold standard for measuring IR, its complexity and invasive nature render it unsuitable for clinical research. The triglyceride-glucose (TyG) index, as an alternative indicator of IR, has shown remarkable superiority in predicting cardiovascular disease (CVD) risk and prognosis [13–18]. Recently, a novel biomarker, cholesterol, high-density lipoprotein, and glucose (CHG) index, has been introduced for diagnosing type 2 diabetes. Compared to the TyG index, the CHG index demonstrates superior diagnostic performance for type 2 diabetes and comparable predictive ability for CVD risk [19, 20]. However, no studies have yet explored the potential of the CHG index in predicting all—cause and cardiovascular mortality risks in patients with calcific aortic stenosis. Our study aims to evaluate the correlation between the CHG index and these mortality risks in such patients.
This study is a retrospective cohort analysis based on patient data from the Aortic valve diseases RISk facTOr assessmenT and prognosis modeL construction (ARISTOTLE) study at the First Affiliated Hospital of Sun Yat-sen University. It develops a novel biomarker for predicting the prognosis of patients with calcific aortic stenosis, offering more references for early identification of high—risk individuals, promoting clinical consultation, and optimizing aortic stenosis prevention decisions.
Methods
Study design and participants
This retrospective cohort study utilized patient data from the Aortic valve diseases RISk facTOr assessmenT and prognosis modeL construction (ARISTOTLE) study. ARISTOTLE is a real-world, multicenter study focusing on inpatients with aortic valve diseases in South China. Its objectives are to measure aortic valve diseases and analyze the risk factors affecting their prognosis. The study is registered at the Chinese Clinical Trial Register (Registration Number: NCT06069232).
Our retrospective research included 1460 patients diagnosed with severe aortic stenosis at the First Affiliated Hospital of Sun Yat-sen University between October 2013 and February 2025. Inclusion required patients to be [21]: (1) Aged over 18; (2) Diagnosed with CAVS via echocardiography as per the guidelines. Mild CAVS was defined as a peak aortic jet velocity (Vmax) of 2.6–2.9 m/s, a mean aortic pressure gradient (MG) of less than 20 mmHg, or an aortic valve area (AVA) of over 1.5 cm2. Moderate CAVS was classified as a Vmax of 3.0–4.0 m/s, an MG of 20–40 mmHg, or an AVA of 1.0–1.5 cm2. Severe CAVS was determined by a Vmax exceeding 4.0 m/s, an MG over 40 mmHg, or an AVA below 1.0 cm2. The severity of aortic stenosis in all patients was reclassified according to the guidelines instead of being directly recorded based on the echocardiogram reports.
Patients were excluded if they: (1) Loss of follow-up (N = 93); (2) Had a diagnosis of rheumatic heart disease (N = 104); (3) Had a history of AVR at baseline (N = 44); (4) Lacked variables needed to calculate CHG index (N = 15); (5) Had missing values for other covariates (N = 29). Ultimately, 1175 CAVS patients were included for subsequent analysis (Fig. 1). Loss to follow-up was defined as the inability to contact the patient or their family by telephone before the end of the follow-up period, resulting in uncertainty about whether all-cause or cardiovascular death had occurred. This study was conducted in line with the Declaration of Helsinki and had the approval of the Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University. All clinical data was sourced from electronic medical records.
Fig. 1.
Flow chart for selecting patients with aortic stenosis from ARISTOTLE study for analysis
Cholesterol, high-density lipoprotein, and glucose (CHG) index
Total cholesterol (TC), fasting blood glucose (FBG), and high-density lipoprotein cholesterol (HDL-C) were obtained from electronic medical records, measured using an automatic biochemistry analyzer (Model: AU5800, Beckman Coulter, Inc., USA). The above biochemical indicators are the results of fasting blood drawn at 6:00 am on the second day after hospitalization. The CHG index was calculated using the formula: CHG index = Ln [TC (mg/dL) × FBG (mg/dL)/2 × HDL-C (mg/dL)] [19, 20]. A cutoff value of 5.276 for the CHG index was determined optimal via ROC curve analysis (Supplementary Fig. 1).
Definitions of endpoints
The endpoints were all-cause and cardiovascular mortality, collected by trained medical staff via phone calls to patients or their families. Follow-up duration was calculated from the CAVS diagnosis date to the death date or last follow-up. All patients were followed up by April 2025.
Covariates
Baseline clinical data, including age, sex, smoking/alcohol history, medication use, and history of hypertension, diabetes, coronary heart disease (CHD), stroke, atrial fibrillation (AF), chronic kidney disease (CKD), and aortic valve replacement (AVR), were self-reported and verified by healthcare professionals during hospitalization. Transthoracic echocardiography (TTE) data from the First Affiliated Hospital of Sun Yat-sen University echocardiography department were used to record parameters like left ventricular ejection fraction (LVEF), bicuspid aortic valve (BAV) status, Vmax, AVA, and MG. CAVS severity was classified per the American Society of Echocardiography guidelines. Serum creatinine (Scr), low-density lipoprotein cholesterol (LDL-C), albumin (ALB) and lipoprotein[a] (Lp[a]) were measured from overnight-fasted venous blood samples using the aforementioned auto-biochemistry analyzer. estimated glomerular filtration rate (EGFR) was calculated via the CKD-EPI 2021 equation: EGFR = 142 × min(Scr/k,1)α × max(Scr/k,1) − 1.200 × 0.9938age × 1.012 (if female), where Scr is serum creatinine, k is 0.7 for females and 0.9 for males, α is − 0.241 for females and − 0.302 for males, min indicates the minimum of Scr/k or 1, max indicates the maximum of Scr/k or 1. The above biochemical indicators are the results of fasting blood drawn at 6:00 am on the second day after hospitalization. Hypertension was defined as SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg, or use of antihypertensive medications. Diabetes was defined as FBG ≥ 7.0 mmol/L, HbA1c ≥ 6.5%, or self-reported diabetes history. Stroke was self-reported or indicated by ongoing stroke treatment. History of chronic kidney disease, atrial fibrillation, and coronary heart disease was also self-reported.
Statistical analysis
Continuous variables with normal distribution or abnormal distribution were summarized as mean (standard deviation) or median [interquartile range], while categorical variables were presented as counts (percentages). As no established clinical cutoff for CHG index exists, the optimal cutoff for predicting primary endpoints was determined via ROC curve analysis. The Kaplan–Meier method estimated cumulative incidence of all—cause and cardiovascular mortality, with log—rank tests for group differences.
Multivariable-adjusted Cox models were used to assess the relationship between the CHG index and mortality risks. Model 1 was unadjusted. Model 2 adjusted for age and gender. Model 3 added LVEF, BAV, BMI, LDL-C, EGFR, Lp[a], ALB, smoking, and alcohol use. Model 4 further adjusted for hypertension, diabetes, stroke, atrial fibrillation, chronic kidney disease, and coronary heart disease. Model 5 included adjustments for antihypertension medication, hyperglycemic medication, lipid-lowering medication and aortic valve replacement. Subgroup analyses across various factors (age, gender, BAV, CAVS severity, smoking, alcohol, hypertension, diabetes, stroke, atrial fibrillation, chronic kidney disease, aortic valve replacement) checked the consistency of CHG index prognostic impact. All analyses were performed using R 4.3.3 and Stata 17.0, with two—sided p < 0.05 indicating statistical significance.
Result
Baseline characteristics
CHG index was a binary variable. Table 1 shows the baseline characteristics of the 1175 patients, including anthropometric data, venous blood biochemical characteristics, and echocardiographic data. The patients had a mean age of 68.91 ± 11.68 years, and 56.6% were male. Compared to the low CHG index group, the high CHG index group presented with a significantly higher BMI and lower peak aortic jet velocity, they also exhibited lower serum albumin, HDL-C and eGFR, alongside higher fasting blood glucose, total cholesterol, LDL-C and lipoprotein(a) levels. In terms of comorbidities, the high CHG index group had a greater prevalence of hypertension, type 2 diabetes and coronary heart disease, a higher proportion were already on hypoglycaemic and lipid-lowering therapy, while fewer had not undergone any aortic valve replacement.
Table 1.
Baseline clinical features of patients stratified by CHG index
| Level | Overall | Low CHG index | High CHG index | p | |
|---|---|---|---|---|---|
| n | 1175 | 585 | 590 | ||
| Demographic data | |||||
| Age | 68.906 (11.68) | 68.326 (12.30) | 69.480 (11.00) | 0.091 | |
| Gender (%) | Female | 510 (43.40) | 269 (45.98) | 241 (40.85) | 0.086 |
| Male | 665 (56.60) | 316 (54.02) | 349 (59.15) | ||
| BMI, kg/m2 | 23.15 [20.82, 25.92] | 22.43 [20.20, 24.91] | 23.85 [21.34, 26.62] | < 0.001 | |
| Behavioral factors | |||||
| Smoking (%) | No | 831 (70.72) | 421 (71.97) | 410 (69.49) | 0.386 |
| Yes | 344 (29.28) | 164 (28.03) | 180 (30.51) | ||
| Drinking (%) | No | 1005 (85.53) | 500 (85.47) | 505 (85.59) | 1 |
| Yes | 170 (14.47) | 85 (14.53) | 85 (14.41) | ||
| Comorbidities | |||||
| Hypertension (%) | No | 550 (46.81) | 319 (54.53) | 231 (39.15) | < 0.001 |
| Yes | 625 (53.19) | 266 (45.47) | 359 (60.85) | ||
| Type 2 diabetes mellitus (%) | No | 883 (75.15) | 492 (84.10) | 391 (66.27) | < 0.001 |
| Yes | 292 (24.85) | 93 (15.90) | 199 (33.73) | ||
| CHD (%) | No | 762 (64.85) | 402 (68.72) | 360 (61.02) | 0.007 |
| Yes | 413 (35.15) | 183 (31.28) | 230 (38.98) | ||
| Stroke (%) | No | 1047 (89.11) | 531 (90.77) | 516 (87.46) | 0.084 |
| Yes | 128 (10.89) | 54 (9.23) | 74 (12.54) | ||
| AF (%) | No | 957 (81.45) | 475 (81.20) | 482 (81.69) | 0.885 |
| Yes | 218 (18.55) | 110 (18.80) | 108 (18.31) | ||
| CKD (%) | No | 1035 (88.09) | 523 (89.40) | 512 (86.78) | 0.195 |
| Yes | 140 (11.91) | 62 (10.60) | 78 (13.22) | ||
| Treatment | |||||
| Antihypertensive medication (%) | No | 148 (12.60) | 81 (13.85) | 67 (11.36) | 0.231 |
| Yes | 1027 (87.40) | 504 (86.15) | 523 (88.64) | ||
| Hyperglycemic medication (%) | No | 866 (73.70) | 495 (84.62) | 371 (62.88) | < 0.001 |
| Yes | 309 (26.30) | 90 (15.38) | 219 (37.12) | ||
| Lipid-lowering medication (%) | No | 575 (48.94) | 326 (55.73) | 249 (42.20) | < 0.001 |
| Yes | 600 (51.06) | 259 (44.27) | 341 (57.80) | ||
| AVR (%) | No | 663 (56.43) | 306 (52.31) | 357 (60.51) | 0.001 |
| TAVR | 213 (18.13) | 98 (16.75) | 115 (19.49) | ||
| SAVR | 299 (25.45) | 181 (30.94) | 118 (20.00) | ||
| Echocardiographic data | |||||
| LVEF, % | 66.00 [57.00, 72.00] | 66.00 [58.00, 73.00] | 65.00 [56.00, 72.00] | 0.047 | |
| Vmax, m/s | 3.65 [2.80, 4.40] | 3.780 [2.90, 4.00] | 3.50 [2.70, 4.33] | 0.002 | |
| CAVS Degree (%) | Mild CAVS | 329 (28.00) | 151 (25.81) | 178 (30.17) | 0.089 |
| Moderate CAVS | 375 (31.91) | 182 (31.11) | 193 (32.71) | ||
| Severe CAVS | 471 (40.09) | 252 (43.08) | 219 (37.12) | ||
| BAV (%) | No | 1108 (94.30) | 552 (94.36) | 556 (94.24) | 1 |
| Yes | 67 (5.70) | 33 (5.64) | 34 (5.76) | ||
| Laboratory parameters | |||||
| LDL-C, mmol/L | 2.52 [2.00, 3.25] | 2.290 [1.87, 2.84] | 2.86 [2.26, 3.56] | < 0.001 | |
| TC, mmol/L | 4.10 [3.40, 5.00] | 3.800 [3.20, 4.60] | 4.50 [3.60, 5.40] | < 0.001 | |
| HDL-C, mmol/L | 40.98 [34.79, 49.49] | 44.459 [37.50, 52.96] | 38.08 [32.47, 45.62] | < 0.001 | |
| Lp[a], mmol/L | 168.00 [76.00, 378.00] | 148.00 [70.00, 350.00] | 185.00 [87.00, 399.0] | 0.008 | |
| FBG, mmol/L | 97.20 [84.60, 126.00] | 86.40 [79.20, 97.20] | 122.40 [97.20, 154.80] | < 0.001 | |
| EGFR, ml/min/1.73 m2 | 77.07 [56.29, 94.13] | 78.87 [59.25, 94.91] | 75.486 [54.53, 92.67] | 0.006 | |
| ALB, g/L | 38.00 [35.00, 41.00] | 38.50 [35.10, 41.40] | 38.000 [34.33, 41.00] | 0.056 | |
Continuous variables with normal distribution are expressed as mean (SD), while non-normally distributed continuous variables are expressed as median [interquartile range (IQR)]. Categorical variables are presented as n (%). Baseline characteristics of 1175 patients from the ARISTOTLE study, stratified by CHG index.
CHG index, cholesterol, high-density lipoprotein, and glucose index; LVEF, left ventricular ejection fraction; Vmax, peak aortic jet velocity; CAVS, calcific aortic valve stenosis; EGFR, estimated glomerular filtration rate; BMI, body mass index; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; FBG, fasting blood glucose; Lp[a], lipoprotein[a]; ALB, albumin; BAV, bicuspid aortic valve; CHD, coronary atherosclerotic heart disease; AF, atrial fibrillation; CKD, chronic kidney disease; AVR, aortic valve replacement; TAVR, transcatheter aortic valve replacement; SAVR, surgical aortic valve replacement. Hyperglycemic medication include: biguanides, sulfonylurea secretagogues, non-sulfonylurea secretagogues, alpha-glucosidase inhibitors, Thiazolidinediones, DPP-4 inhibitors, sodium-dependent glucose transporters 2 (SGLT-2) inhibitors, and insulin and its analogues. Lipid-lowering medication include: statins, fibrates, cholesterol absorption inhibitors, proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, niacin, cholestyramine (bile acid sequestrants), omega-3 polyunsaturated fatty acids, and traditional Chinese medicine for lipid-lowering.
The correlation between CHG index and cardiovascular mortality and all-cause mortality in patients with CAVS
During a median follow-up of 3.23 [1.15, 6.07] years, 179 cardiovascular deaths and 329 all-cause deaths occurred. The high CHG group had higher cardiovascular mortality (19.80% vs. 15.15%) and all-cause mortality (30.68% vs. 25.30%) rates. There were significant differences in cardiovascular mortality (p = 0.0048) and all-cause mortality (p = 0.0014) between the two groups (Fig. 2).
Fig. 2.
Cardiovascular mortality (A) and all-cause mortality (B) in the high CHG index and low CHG index groups
Table 2 presents a Cox regression analysis of the correlation between the CHG index and cardiovascular and all-cause mortality in patients with CAVS. In the unadjusted model (Model 1), the CHG index was significantly associated with cardiovascular and all-cause mortality in these patients, and this significant association persisted in models 2, 3, 4, and 5. In the fully adjusted model, each 1—unit increase in the CHG index was linked to a 53% higher risk of cardiovascular mortality (HR 1.53, 95% CI 1.078–2.163, p = 0.017) and a 43% higher risk of all-cause mortality (HR 1.43, 95% CI 1.108–1.840, p = 0.006). Moreover, compared to the low CHG index group, the high CHG index group had a 1.44—fold higher risk of cardiovascular mortality (HR 1.44, 95% CI 1.018–2.024, p = 0.039) and a 1.429—fold higher risk of all-cause mortality (HR 1.43, 95% CI 1.111–1.839, p = 0.005).
Table 2.
Association between CHG index and all-cause mortality and cardiovascular mortality in the CAVS patients*
| Cardiovascular death | Low CHG index | High CHG index | Continuous (per unit) | |||
|---|---|---|---|---|---|---|
| HR (95CI) | p | HR (95CI) | p | HR (95CI) | p | |
| Model 1 | Ref | – | 1.526 (1.135–2.052) | 0.005 | 1.532 (1.146–2.049) | 0.004 |
| Model 2 | Ref | – | 1.485 (1.105–1.998) | 0.009 | 1.551 (1.151–2.090) | 0.004 |
| Model 3 | Ref | – | 1.406 (1.017–1.944) | 0.039 | 1.587 (1.137–2.217) | 0.007 |
| Model 4 | Ref | – | 1.388 (0.991–1.943) | 0.057 | 1.477 (1.045–2.090) | 0.027 |
| Model 5 | Ref | – | 1.435 (1.018–2.024) | 0.039 | 1.527 (1.078–2.163) | 0.017 |
| All-cause death | Low CHG index | High CHG index | Continuous (per unit) | |||
|---|---|---|---|---|---|---|
| HR (95CI) | p | HR (95CI) | p | HR (95CI) | p | |
| Model 1 | Ref | – | 1.423 (1.145–1.770) | 0.001 | 1.307 (1.047–1.632) | 0.018 |
| Model 2 | Ref | – | 1.380 (1.110–1.715) | 0.004 | 1.320 (1.052–1.656) | 0.016 |
| Model 3 | Ref | – | 1.275 (1.004–1.619) | 0.046 | 1.277 (0.993_1.643) | 0.057 |
| Model 4 | Ref | – | 1.309 (1.022–1.676) | 0.033 | 1.278 (0.986–1.658) | 0.064 |
| Model 5 | Ref | – | 1.429 (1.111–1.839) | 0.005 | 1.428 (1.108–1.840) | 0.006 |
*Cox regressions with Firth’s penalized maximum likelihood method were used
Model 1 Unadjusted model.
Model 2 Adjusted for age and gender.
Model 3 Adjusted for model 2 + peak aortic jet velocity, LV ejection fraction, estimated glomerular filtration rate, body mass index, low-density lipoprotein cholesterol, albumin, lipoprotein[a], bicuspid aortic valve, smoking and drinking.
Model 4 Adjusted for model 3 + diabetes, hypertension, coronary atherosclerotic heart disease, stroke, atrial fibrillation, chronic kidney disease.
Model 5 Adjusted for model 4 + antihypertension medication, hyperglycemic medication, lipid-lowering medication and aortic valve replacement.
CHG, Cholesterol, high-density lipoprotein, and glucose index; HR, hazard ratio; CI, confidence interval.
Multivariable adjusted restricted cubic splines regression models also showed a linear association between the CHG index with the risk of cardiovascular mortality (p for non-linearity = 0.529) and all-cause mortality (p for non-linearity = 0.436) (Fig. 3). Subgroup analyses were performed based on bicuspid aortic valve (yes or no), gender (male or female), age (> 65 or ≤ 65 years), aortic stenosis severity (mild or moderate or severe), smoking (yes or no), alcohol consumption (yes or no), hypertension (yes or no), diabetes (yes or no), coronary heart disease (yes or no), stroke (yes or no), atrial fibrillation (yes or no), chronic kidney disease (yes or no), use of antihypertensive or antidiabetic or lipid-lowering drugs (yes or no), and AVR (none or TAVR or SAVR) (Fig. 4). After adjusting for confounders in all subgroups, the CHG index showed consistent effects on cardiovascular and all-cause mortality in patients with CAVS, with no significant interactions between subgroups (all interaction p > 0.05).
Fig. 3.
Adjusted hazard ratios for incident cardiovascular mortality (A) and all-cause mortality (B) by baseline CHG index
Fig. 4.
Subgroup analysis of the association between CHG index and a cardiovascular mortality (A) and all-cause mortality (B)
Discussion
Our research has for the first time demonstrated the association between the CHG index and cardiovascular mortality and all-cause mortality in patients with CAVS. The relationship was linear across the entire CHG spectrum and remained consistent across key subgroups, including those already classified as having severe stenosis by echocardiography.
The epidemiological evidence from Sweden has played a significant role in establishing that dysglycemia is an independent driving factor for aortic valve disease, including hyperglycaemia and insulin resistance. In a large cross-sectional study, prediabetes and newly diagnosed diabetes were associated with a significantly higher burden of aortic valve calcification [8]. Lind et al. analyzed 320,000 adults from the AMORIS cohort and documented dose–response associations: compared with normoglycaemia, impaired fasting glucose (6.1–6.9 mmol/L) increased the risk of incident CAVS by 36%, whereas overt diabetes doubled the risk [10]. Poorly controlled diabetes promotes the accumulation of advanced glycation end products (AGEs) and their receptor (RAGE) in stenotic valves, thereby exacerbating oxidative stress and extracellular matrix remodeling [9]. Our study extends this line of research further by showing that even quantification of insulin resistance by simple fasting blood metrics translates into excess mortality in patients with established CAVS.
The alternative indicators of insulin resistance are mostly composite indicators of blood glucose and/or blood lipids, including the TyG-BMI index [22, 23], TyG index variability[24], TyG-WHR index [25], TyG-WC index [26, 27], and the TG/HDL-C ratio [28]. These indicators are independent risk factors for cardiovascular events. In valve disease research, the TyG index and its derivatives have been the most extensively studied. Previous work demonstrated that each 1-unit increase in TyG index was associated with a 35% increase in all-cause mortality among patients with moderate-to-severe aortic stenosis [12], and a high TyG-BMI index predicted significant disease progression in non-severe CAVS [11]. Our study found that CHG index was an independent risk factor for cardiovascular and all-cause mortality in CAVS patients. However, the CHG index incorporates HDL-C, thereby capturing additional pathobiology: HDL-C is not merely an inverse correlate of insulin resistance but also a direct marker of antioxidant and anti-inflammatory capacity. The observation that HDL-C levels decline in parallel with stenosis severity [29], together with evidence that dysfunctional HDL accelerates valvular calcification via myeloperoxidase-dependent oxidation [30], provides mechanistic support for the superior risk discrimination observed with CHG index. These findings underscore the rationale for including HDL-C in the CHG index.
The pathological mechanism of insulin resistance and poor prognosis of calcified aortic valve stenosis is still unclear, but several possible mechanisms have been proposed at present: (1) Endothelial dysfunction. The imbalance between the synthesis and release of vasodilator and constrictor factors by endothelial cells promotes the development of atherosclerosis, affects the aortic valve and vascular function and structure, and increases the risk of adverse prognosis [31]. Meanwhile, the inflammatory response runs through the entire process of the onset and progression of aortic valve stenosis. Components such as cholesterol crystals are phagocytosed by macrophages to form foam cells. After rupture, they release inflammatory mediators, attracting inflammatory cell infiltration and triggering chronic inflammation. High cholesterol and high blood sugar induce inflammatory responses, intensify the local inflammatory process of the aortic valve, increase the degree of aortic valve lesions, and raise the risk of cardiovascular events [20, 32–34]. (2) Oxidative stress and inflammatory response. When the oxidative stress state in the body increases, the excessive reactive oxygen species produced can damage cellular biomolecules, promote the proliferation and phenotypic transformation of valvular interstitial cells, and lead to valve thickening, stiffness and calcification. Hypercholesterolemia and hyperglycemia are both closely related to oxidative stress, which aggravates the progression of aortic valve stenosis and cardiovascular lesions. In addition, insulin resistance can cause metabolic disorders, including elevated blood glucose, dyslipidemia, elevated blood pressure, etc., increase the metabolic burden on the heart, promote myocardial hypertrophy and fibrosis, and affect cardiac function [35, 36]. The increase of CHG index indicates abnormal metabolism in the body. The synergistic effect of insulin resistance and lipid and blood glucose disorders promotes the occurrence of poor prognosis in patients with aortic valve stenosis. (3) Risk of thrombosis. Hemodynamic changes during aortic valve stenosis increase the risk of thrombosis, and factors such as endothelial dysfunction keep the blood in a hypercoagulable state. Hypercholesterolemia and hyperglycemia affect the hemorheological properties and promote platelet aggregation and thrombosis [37]. The CHG index is interwoven through multiple pathological mechanisms and jointly affects the prognosis of patients with aortic valve stenosis. In the future, the interaction among these mechanisms can be deeply studied to provide new targets for improving the prognosis of patients.
Current guidelines primarily rely on echocardiographic peak velocity, mean pressure gradient, and valve area for risk stratification, but inconsistent grading in some patients complicates intervention timing decisions. In the ARISTOTLE cohort, we first confirmed that when CHG index ≥ 5.276, cardiovascular mortality risk of CAVS patients increased 1.44-fold, and all-cause mortality risk increased 1.43-fold, independent of echocardiographic severity grading. Thus, CHG index can serve as a non-echocardiographic supplementary metric to identify patients with high metabolic risk and preliminarily assess TAVR/SAVR intervention timing. Additionally, the CHG index measurement is simple, requiring only fasting lipid and glucose levels, and can be calculated simultaneously for patients with moderate or greater aortic stenosis on echocardiography. Patients with a high CHG index should receive intensive risk factor control, shortened follow-up intervals, and, if necessary, combined CT calcium scoring assessment.
Our study has the following limitations. First, although we made multivariate adjustments in the cox regression model, there is still the possibility of residual or measured confounding bias. Second, we only included the Chinese elderly population, and further validation in other ethnic and younger populations is needed. In the future, we will continue to increase the sample size and add multi-center and ethnically diverse as well as youth participants to enhance the credibility of the analysis. Third, the blood test data only corresponds to the admission test data, and there may be deviations caused by measurement errors. Fourth, observational studies cannot assess the causal relationship between the CHG index and all-cause mortality and cardiovascular mortality in patients with calcifying aortic valve stenosis. Further basic and clinical studies are needed to verify the reliability of our current results. Fifth, future prospective studies should be designed to compare the CHG index, TyG index, and HOMA in parallel within the same population, in order to further delineate the optimal scenarios for each marker. Future longitudinal studies or potential intervention studies can further strengthen the discussion on future directions. Last, although the CHG index showed additional prognostic value in the CAVS population, its AUC was only in the moderate range, suggesting that the combination of valve anatomy or inflammation measures is still needed to improve discrimination.
Conclusion
Higher CHG index is associated with follow-up cardiovascular mortality and all-cause mortality in patients with calcifying aortic valve stenosis. The research results show that the CHG index can be used as an indicator to evaluate the prognosis of patients with calcific aortic valve stenosis, providing more references for promoting clinical decisions and optimizing the prevention decisions of aortic valve stenosis.
Supplementary Information
Below is the link to the electronic supplementary material.
Supplementary Fig. 1. The ROC curve analysis determined the optimal cutoff value of the CHG index for predicting all-cause mortality(A) and cardiovascular mortality (B) in patients with CAVS.
Acknowledgements
We thank all patients who generously took the time and donated samples to participate in this study.
Abbreviations
- CHG index
Cholesterol, high-density lipoprotein, and glucose index
- CAVS
Calcific aortic valve stenosis
- ARISTOTLE
Aortic valve diseases RISk facTOr assessmenT andprognosis modeL construction
- IR
Insulin resistance
- AVR
Aortic valve replacement
- AVC
Aortic valve calcification
- HEC
Hyperinsulinemic-euglycemic clamp
- CVD
Cardiovascular disease
- Vmax
Peak aortic jet velocity
- MG
Mean aortic pressure gradient
- AVA
Aortic valve area
- TC
Total cholesterol
- FBG
Fasting blood glucose
- HDL-C
High-density lipoprotein and cholesterol
- CHD
Coronary heart disease
- AF
Atrial fibrillation
- CKD
Chronic kidney disease
- TTE
Transthoracic echocardiography
- LVEF
Left ventricular ejection fraction
- BAV
Bicuspid aortic valve
- LDL-C
Low-density lipoprotein cholesterol
- ALB
Albumin
- Lp[a]
Lipoprotein[a]
- Scr
Serum creatinine
- EGFR
Estimated glomerular filtration rate
- SBP
Systolic blood pressure
- DBP
Diastolic blood pressure
- HbA1c
Glycosylated Hemoglobin, Type A1C
- SD
Standard deviation
- ROC
Receiver operating characteristic
- HR
Hazard ratio
- 95%CI
95% Confidence
- Lp[a]
Lipoprotein[a]
Author contributions
X. L., X. Z., Z. G. and Z. X. contributed to the conception or design of the work. All authors were responsible for the acquisition, analysis and interpretation of data. X. L., X. Z., Z. G. and W. Z. drafted the manuscript. Critical revision of the manuscript for important intellectual content were performed by all authors. All authors agreed with the content of the article to be submitted.
Funding
National Natural Science Foundation of China (82200408 to J.Li), Young Science and Technology Talent Support Program of Guangdong Precision Medicine Application Association (YSTTGDPMAA202502 to Z.Xiong), Guangdong Basic and Applied Basic Research Foundation (2024A1515012356 to X.Z.), NSFC Incubation Project of Guangdong Provincial People's Hospital (KY0120220034 to J.Li).
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
This study was approved by the Ethics Review Committee of the First Affiliated Hospital of Sun Yat-Sen University. Patient follow-up was conducted via telephone contact, with verbal informed consent approved by the institutional ethics committee.
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.
Zhen Guo, Zhenyu Xiong and Wenjing Zhang have contributed equally to this work.
Contributor Information
Xinxue Liao, Email: liaoxinx@mail.sysu.edu.cn.
Xiaodong Zhuang, Email: zhuangxd3@mail.sysu.edu.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Fig. 1. The ROC curve analysis determined the optimal cutoff value of the CHG index for predicting all-cause mortality(A) and cardiovascular mortality (B) in patients with CAVS.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.