Abstract
Objective
To investigate the association between sarcopenia and ventricular remodelling after coronary artery bypass grafting (CABG) in elderly patients with coronary heart disease (CHD).
Methods
A total of 135 elderly patients with CHD admitted to hospital between February 2021 and May 2023 were prospectively selected. According to whether ventricular remodelling occurred during the follow-up period, patients were divided into an occurrence group and a non-occurrence group.
Results
The incidence of ventricular remodelling after CABG in elderly patients with CHD was 23.70% (32/135). Significant differences between the two groups were observed in smoking history, diabetes history, prevalence of sarcopenia, degree of preoperative coronary stenosis, levels of lipoprotein (a) ([Lp(a)]), uric acid (UA), protease-activated receptor 2 (PAR2), monocyte-to-HDL ratio (MHR), and suspended red blood cell input (P < 0.05). Multivariable logistic regression analysis identified smoking history [odds ratio (OR) = 2.186, 95% confidence interval (CI) 1.34–3.57], diabetes history (OR = 2.171, 95% CI 1.32–3.58), sarcopenia (OR = 2.230, 95% CI 1.37–3.62), a high degree of preoperative coronary stenosis (OR = 2.223, 95% CI 1.36–3.64), elevated Lp(a) (OR = 2.143, 95% CI 1.32–3.49), elevated UA (OR = 2.164, 95% CI 1.31–3.58), elevated PAR2 (OR = 2.192, 95% CI 1.32–3.64), and elevated MHR (OR = 2.201, 95% CI 1.32–3.68) as independent risk factors for ventricular remodelling after CABG in the study population. Analysis showed that sarcopenia predicted ventricular remodelling with a sensitivity of 90.0%, specificity of 47.0%, and an area under the curve of 0.772 (95% CI 0.68–0.86, P < 0.0001).
Conclusion
Sarcopenia is a risk factor affecting ventricular remodelling after CABG in elderly patients and shows strong predictive efficacy in this population.
Keywords: coronary heart disease, elderly patients, coronary artery bypass grafting, left ventricular remodelling, sarcopenia
1. Introduction
With the ageing of modern society, the incidence of coronary heart disease (CHD) in the elderly population continues to rise each year. The disease remains incurable, and current clinical approaches primarily aim to relieve symptoms and prolong survival through medication, interventional surgery, and coronary artery bypass grafting (CABG) (1, 2).
Percutaneous coronary intervention is widely used in the treatment of CHD in the elderly due to its advantages of minimal trauma, rapid postoperative recovery, and high safety. Conversely, CABG is the preferred option for more serious CHD, such as stenosis of 70% or more of the coronary artery trunk and branches, myocardial infarction accompanied by ventricular aneurysm, or valvular closure insufficiency (3, 4).
Coronary artery bypass grafting is an effective clinical revascularisation technique for severe coronary lesions, and with continuous improvements in surgical quality, the survival rate of elderly patients has increased considerably (5). However, oedema and necrosis of cardiomyocytes may occur secondary to CABG in elderly patients with CHD, further altering endocrine regulation, neuromodulation, and local myocardial motion and function. These pathological changes ultimately lead to ventricular remodelling and compromise long-term prognosis (6). Early identification and control of risk factors affecting ventricular remodelling are, therefore, crucial for improving outcomes in this population.
Research has found that a decline in skeletal muscle quality, strength, or function is associated with a poor prognosis (7). Sarcopenia, defined as a syndrome characterised by generalised muscle loss, decreased muscle strength, and impaired physiological function, is a major contributor to cardiopulmonary decline and bone fractures in the elderly (8). The co-morbidity of CHD and sarcopenia is relatively common: CHD can promote sarcopenia development through oxidative stress, autophagy, apoptosis, and inflammation, and sarcopenia, in turn, affects cardiovascular prognosis by impairing muscle strength and metabolic regulation (9–11). Recent studies have shown that patients with concurrent sarcopenia have a higher incidence of major adverse cardiac and cerebral events and shorter survival after CABG (12). Moreover, skeletal muscle deterioration may aggravate insulin resistance and atherosclerosis or interact with neurohormonal and inflammatory pathways, thereby increasing the risk of ventricular remodelling after CABG (13, 14).
Previous research has linked sarcopenia or frailty to mortality, morbidity, and delayed recovery after cardiac procedures. However, whether sarcopenia independently predicts ventricular remodelling after CABG and how it integrates with inflammatory–metabolic markers to improve risk discrimination, remains insufficiently defined, particularly within Asian cohorts.
The present study prospectively evaluates elderly Chinese patients undergoing CABG and focuses on postoperative ventricular remodelling as the primary outcome (change in left ventricular end-diastolic volume [ΔLVEDV] ≥ 15%). In addition to sarcopenia, protease-activated receptor 2 (PAR2) and the monocyte-to-HDL ratio (MHR) are examined as adjunct markers that may capture inflammation- and metabolism-related risk pathways implicated in remodelling. By reporting both discrimination (receiver operating characteristic [ROC]/area under the curve [AUC]) and calibration (Hosmer–Lemeshow test, calibration intercept/slope), the aim is to provide transparent, clinically interpretable model performance and a theoretical basis for better clinical guidance of post-CABG rehabilitation in elderly patients with CHD, as reported below.
1.1. Information and methods
1.1.1. Sample size justification
Before enrolment, an a priori power calculation for the primary endpoint (post-CABG ventricular remodelling, defined as ΔLVEDV ≥ 15%) was performed. Assuming a remodelling incidence of 30% in the overall cohort, an expected risk increase associated with sarcopenia corresponding to an odds ratio (OR) = 2.0, two-sided α = 0.05, and 80% power, the minimum required sample size was approximately 128 participants to provide a balanced and effective sample for logistic regression. To allow for potential loss to follow-up and measurement uncertainty, the target sample size was set at ≥130 participants. The final cohort included 135 patients, meeting the prespecified power requirement.
1.1.2. General information
A total of 135 elderly patients with CHD who were admitted to hospital between February 2021 and May 2023 were prospectively selected. The inclusion criteria were as follows: (1) age ≥60 years, (2) diagnosis of CHD confirmed by coronary angiography, (3) fulfilment of surgical indications for CABG and successful completion of the procedure, (4) adherence to prescribed postoperative medication, and (5) provision of signed informed consent for study participation. The exclusion criteria were as follows: (1) active bleeding, gastrointestinal haemorrhage, or cerebral haemorrhage within the past 3 months; (2) history of cardiogenic syncope, cardiogenic shock, or severe cardiac arrhythmia; (3) the presence of a malignant tumour, severe immune system disease, or hepatic or renal insufficiency; and (4) the presence of cognitive dysfunction, bipolar disorder, or other psychiatric disorders. The hospital ethics committee reviewed and approved the study protocol prior to implementation.
2. Methods
2.1. Criteria for the assessment of sarcopenia
The diagnosis of sarcopenia followed the consensus recommendations of the European Working Group on Sarcopenia in Older People 2 (2019) (15) and the Asian Working Group for Sarcopenia (2019) (16). The diagnostic framework included 3 components: low muscle mass, low muscle strength, and low physical performance. In our study, the following cutoffs were applied: (1) relative skeletal muscle mass index (SMI): <7.0 kg/m² for men and <5.7 kg/m² for women, (2) handgrip strength (dominant hand): <26 kg for men and <18 kg for women, and (3) gait speed: <0.8 m/s for both men and women. Participants with low muscle mass [criterion (1)] combined with either low muscle strength [criterion (2)] or low physical performance [criterion (3)] were classified as having sarcopenia.
2.2. Criteria for assessment of ventricular remodelling
Echocardiography was performed using a diagnostic echocardiograph to record LVEDV. The LVEDV growth rate was calculated from two echocardiographic measurements as follows:
A ΔLVEDV ≥15% was defined as ventricular remodelling, consistent with existing literature, demonstrating its prognostic significance (e.g., hazard ratio: 2.1, P = 0.007) (8, 17, 18).
2.3. Follow-up and grouping
Following surgery, patients received regular follow-ups until November 2023. The median follow-up duration was 12 months (range, 6–18 months), conducted through a combination of outpatient visits and telephone interviews. Standardised echocardiographic assessments were performed at baseline (within 1 week after surgery) and at 3, 6, and 12 months, with additional evaluations if cardiac symptoms occurred.
According to the criteria in section 1.3.2, patients who developed ventricular remodelling during the follow-up period were included in the occurrence group, and the rest were included in the non-occurrence group. Clinical data were compared between the two groups.
2.4. Clinical data collection
Patient data during hospitalisation were retrieved from the hospital's electronic medical record system by a single nurse who was blinded to the patient grouping. The records included the following data: general information (gender, age, body mass index, history of smoking and alcohol consumption, past medical history [hypertension, diabetes mellitus, dyslipidaemia], and presence of sarcopenia; lesion characteristics (location, type, branching, degree of coronary artery stenosis); biochemical profiles at admission (total cholesterol, triglycerides, lipoprotein (a) [Lp(a)], aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, creatinine, uric acid [UA], total bilirubin, protease-activated receptor 2 [PAR2], fibrinogen, platelet count, white blood cell count, monocyte count and the calculation of MHR); initial echocardiography (LVED internal diameter and left ventricular ejection fraction); and perioperative indices (number of bridging vessel branches, duration of extracorporeal circulation, duration of aortic block, intraoperative in- and outflow, suspended red blood cell input, postoperative mechanical ventilation time and length of hospital stay).
2.5. Observation indicators
(1) The incidence of ventricular remodelling after CABG in elderly patients with CHD was recorded during the follow-up period. (2) Univariate analysis of potential risk factors for ventricular remodelling after CABG in elderly patients with CHD was performed. (3) Multivariable logistic regression analysis was conducted to clarify the risk factors for ventricular remodelling after CABG in elderly patients with CHD. (4) Participants' ROC curve values were evaluated to assess sarcopenia efficacy in predicting ventricular remodelling after CABG in elderly patients with CHD.
2.6. Statistical analysis
All analyses were conducted in SPSS 26.0 (IBM Corp., Armonk, NY, USA). Continuous variables were summarised as mean ± SD or median (interquartile range) according to normality (Shapiro–Wilk test). Between-group comparisons used one-way ANOVA with LSD post-hoc tests for normally distributed data and the Kruskal–Wallis test for non-normal data. Categorical variables were summarised as counts (%) and compared using the chi-square test or Fisher's exact test, as appropriate.
To assess the association between sarcopenia and postoperative ventricular remodelling, multivariable logistic regression models were fitted. Candidate predictors with clinical relevance and/or P < 0.10 in univariate analyses were entered simultaneously into the multivariable model to obtain adjusted ORs with 95% confidence intervals (CIs). Continuous predictors (e.g., Lp(a), UA, monocyte count, MHR) were z-standardised (value–mean)/SD before modelling to place coefficients on a comparable scale, and categorical covariates (e.g., smoking, diabetes, sarcopenia) were dichotomised using predefined criteria. Sarcopenia was defined using sex-specific cut-offs, and postoperative ventricular remodelling was defined as ΔLVEDV ≥ 15%. Unless stated otherwise, all models used these standardised or coded variables. Model parsimony followed conventional event-per-variable considerations.
2.6.1. Model diagnostics and internal validation
Multicollinearity was evaluated using variance inflation factors (VIF), with VIF < 2.0 considered acceptable. Calibration was examined using the Hosmer–Lemeshow goodness-of-fit test, calibration plots, and calibration intercept/slope. Bootstrap resampling (1,000 iterations) was used to assess model stability and obtain bias-corrected estimates of ORs, 95% CIs, and calibration metrics.
2.6.2. Discrimination and ROC analysis
Discriminative performance was quantified by the AUC with 95% CI. The AUCs were computed for (1) the full multivariable logistic model and (2) individual predictors (e.g., sarcopenia alone) to facilitate clinical interpretation. The AUC comparisons were performed using the DeLong test, and a two-sided P < 0.05 was considered statistically significant. The Brier score was additionally reported as an overall accuracy measure where relevant.
3. Results
3.1. Incidence of ventricular remodelling after coronary artery bypass grafting in elderly patients with coronary heart disease
Follow-up data revealed that the incidence of ventricular remodelling after CABG in elderly patients with CHD was approximately 23.70% (32/135).
3.2. Univariate analysis of risk factors that may influence ventricular remodelling after coronary artery bypass grafting in elderly patients with coronary heart disease
Univariate analysis revealed statistically significant differences between the occurrence group and the non-occurrence group in smoking history, diabetes mellitus, presence of sarcopenia, preoperative degree of coronary stenosis, Lp(a), UA, PAR2, MHR levels, and suspended red blood cell input (P < 0.05) (Table 1).
Table 1.
Univariate analysis of risk factors that may influence ventricular remodelling after coronary artery bypass grafting in elderly patients with CHD.
| Clinical data | Accruals group (n = 32) | Non-occurring group (n = 103) | t/c2 | P |
|---|---|---|---|---|
| Sex (male/female) | 20/12 | 62/41 | 0.054 | 0.816 |
| Age | 68.15 ± 3.27 | 68.03 ± 3.20 | 0.184 | 0.854 |
| BMI (kg/m2) | 21.05 ± 1.23 | 20.95 ± 1.24 | 0.399 | 0.690 |
| Smoking (n, %) | 18 (56.25) | 25 (24.27) | 11.502 | 0.000 |
| Drinking (n, %) | 12 (37.50) | 35 (33.98) | 0.133 | 0.715 |
| Hypertension (n, %) | 8 (25.00) | 21 (20.39) | 0.308 | 0.579 |
| Diabetes (n, %) | 14 (43.75) | 20 (19.42) | 7.672 | 0.006 |
| Dyslipidemia (n, %) | 6 (18.75) | 15 (14.56) | 0.326 | 0.568 |
| Presence of sarcopenia | 9 (28.13) | 7 (6.80) | 10.631 | 0.001 |
| Lesion site | 0.218 | 0.897 | ||
| LAD | 9 (28.13) | 30 (29.13) | ||
| LCX | 12 (37.50) | 42 (40.78) | ||
| RCA | 11 (34.37) | 31 (30.09) | ||
| Lesion typing | 0.285 | 0.867 | ||
| A | 6 (18.75) | 17 (16.50) | ||
| B | 14 (43.75) | 42 (40.78) | ||
| C | 12 (37.50) | 44 (42.72) | ||
| Number of lesion branches | 0.309 | 0.857 | ||
| Single stick | 13 (40.63) | 45 (43.69) | ||
| Bifurcation lesion | 10 (31.25) | 34 (33.01) | ||
| Triple vessel disease | 9 (28.12) | 24 (23.30) | ||
| Preoperative coronary stenosis (%) | 92.20 ± 4.50 | 87.50 ± 4.25 | 5.389 | 0.000 |
| Biochemical profiles at admission | ||||
| TC (mmol/L) | 3.92 ± 0.85 | 3.85 ± 0.82 | 0.418 | 0.676 |
| TG (mmol/L) | 1.79 ± 0.42 | 1.83 ± 0.40 | 0.488 | 0.626 |
| Lp (a) (g/L) | 0.49 ± 0.15 | 0.32 ± 0.12 | 6.582 | 0.000 |
| AST (U/L) | 28.50 ± 6.43 | 29.10 ± 6.50 | 0.457 | 0.648 |
| ALT (U/L) | 24.16 ± 4.20 | 24.80 ± 4.29 | 0.741 | 0.460 |
| ALP (U/L) | 68.50 ± 9.52 | 69.27 ± 9.80 | 0.391 | 0.697 |
| Cr (μmol/L) | 75.93 ± 6.52 | 75.02 ± 6.41 | 0.699 | 0.486 |
| UA (μmol/L) | 465.20 ± 50.70 | 372.15 ± 42.52 | 10.318 | 0.000 |
| TBIL (μmol/L) | 12.05 ± 2.02 | 12.62 ± 2.10 | 1.353 | 0.178 |
| PAR-2 (ng/L) | 813.52 ± 43.50 | 652.70 ± 43.20 | 18.364 | 0.000 |
| FIB (g/L) | 3.07 ± 0.52 | 2.95 ± 0.48 | 1.211 | 0.228 |
| Platelet count (×109/L) | 201.41 ± 15.90 | 202.07 ± 15.95 | 0.205 | 0.838 |
| WBC (×109/L) | 7.32 ± 1.02 | 7.15 ± 1.06 | 0.799 | 0.425 |
| MHR | 0.65 ± 0.13 | 0.42 ± 0.11 | 9.885 | 0.000 |
| Initial echocardiography | ||||
| LVEDd (mm) | 48.60 ± 4.50 | 48.20 ± 4.42 | 0.445 | 0.657 |
| LVEF (%) | 62.50 ± 7.90 | 62.20 ± 7.95 | 0.187 | 0.852 |
| Perioperative indicators | ||||
| Number of bridging vessels (branches) | 3.20 ± 0.50 | 3.35 ± 0.40 | 1.742 | 0.084 |
| Extracorporeal circulation time (min) | 149.50 ± 15.20 | 148.80 ± 15.50 | 0.224 | 0.823 |
| Aortic block time (min) | 97.20 ± 20.50 | 96.50 ± 20.10 | 0.171 | 0.864 |
| Intraoperative volumetric (mL) | 650.34 ± 50.10 | 652.60 ± 51.80 | 0.217 | 0.828 |
| Suspended red blood cell input (U) | 2.50 ± 0.42 | 1.40 ± 0.45 | 12.264 | 0.000 |
| Duration of postoperative mechanical ventilation (h) | 17.20 ± 2.05 | 16.80 ± 2.10 | 0.946 | 0.346 |
| Length of hospitalisation (d) | 25.60 ± 5.10 | 24.90 ± 5.00 | 0.689 | 0.492 |
CHD, coronary heart disease; BMI, body mass index; TC, total cholesterol; TG, triglycerides; Lp(a), lipoprotein(a); AST, aspartate aminotransferase; ALT, alanine aminotransferase; ALP, alkaline phosphatase; Cr, creatinine; UA, uric acid; TBIL, total bilirubin; PAR-2, protease-activated receptor 2; FIB, fibrinogen; MHR, mononuclear cell/HDL-C ratio; LVEDd, left ventricular end-diastolic internal diameter; LVEF, left ventricular ejection fraction.
3.3. Multivariable logistic regression analysis to clarify the risk factors affecting ventricular remodelling after coronary artery bypass grafting in elderly patients with coronary heart disease
Multivariable logistic regression was performed with variables that were statistically different between the occurrence and non-occurrence groups. Independent variables included a history of smoking, diabetes, comorbid sarcopenia, degree of preoperative coronary stenosis, Lp(a), UA, PAR2, MHR, and suspended erythrocyte input (X), with postoperative ventricular remodelling (occurrence = 1, non-occurrence = 0) as the dependent variable (Y).
Independent risk factors for ventricular remodelling after CABG in elderly patients with CHD included a history of smoking (OR = 2.186, 95% CI 1.04–4.07), diabetes mellitus (OR = 2.171, 95% CI 1.32–3.58), comorbid sarcopenia (OR = 2.230, 95% CI 1.37–3.62), a high degree of preoperative coronary stenosis (OR = 2.223, 95% CI 1.36–3.64), elevated Lp(a) (OR = 2.143, 95% CI 1.22–3.49), elevated UA (OR = 2.164, 95% CI: 1.31–3.58), elevated PAR2 (OR = 2.192, 95% CI 1.16–3.84) and a high MHR (OR = 2.201, 95% CI 1.32–3.68) (Table 2).
Table 2.
Risk factors affecting ventricular remodelling after coronary artery bypass grafting in elderly patients with CHD.
| Independent variable | β | SE | Waldx2 | P | OR | 95% CI |
|---|---|---|---|---|---|---|
| Smoking | 0.782 | 0.251 | 9.707 | 0.009 | 2.186 | 1.04–4.07 |
| Diabetes | 0.775 | 0.255 | 9.237 | 0.004 | 2.171 | 1.32–3.58 |
| Presence of sarcopenia | 0.802 | 0.247 | 10.543 | 0.000 | 2.230 | 1.37–3.62 |
| High preoperative coronary stenosis | 0.799 | 0.252 | 10.053 | 0.000 | 2.223 | 1.36–3.64 |
| Lp (a) High level | 0.762 | 0.249 | 9.365 | 0.000 | 2.143 | 1.22–3.49 |
| UA High level | 0.772 | 0.257 | 9.023 | 0.000 | 2.164 | 1.31–3.58 |
| PAR-2 High level | 0.785 | 0.259 | 9.186 | 0.000 | 2.192 | 1.16–3.84 |
| High MHR | 0.789 | 0.262 | 9.069 | 0.000 | 2.201 | 1.32–3.68 |
CHD, coronary heart disease; Lp(a), lipoprotein(a); UA, uric acid; PAR-2, protease-activated receptor 2; MHR, mononuclear cell/HDL-C ratio. Continuous variables were standardized prior to logistic regression. ORs and 95% CIs were calculated from the final multivariable model including all adjusted covariates.
All included variables demonstrated low multicollinearity, with VIF values ranging from 1.097 to 1.842 (Supplementary Table S1). The calibration plot showed good agreement between predicted and observed probabilities, with a calibration slope of 0.97 and an intercept of 0.842, indicating no substantial miscalibration (Supplementary Figure S1).
Sensitivity analyses demonstrated the robustness of the findings. Excluding diabetic patients yielded similar effect sizes for sarcopenia and UA (Supplementary Table S2). Results were directionally consistent across sex strata with no significant interaction (Supplementary Table S3a), and discrimination remained stable under nearby sex-specific cutoffs for sarcopenia (Supplementary Table S3b). A significant sarcopenia × UA interaction indicated increased risk among patients with concomitantly elevated UA (Supplementary Table S3c). All reported ORs represent multivariable-adjusted estimates.
3.4. Efficacy of the receiver operating characteristic curve assessment of sarcopenia in predicting ventricular remodelling after coronary artery bypass grafting in elderly patients with coronary heart disease
The results of the ROC curves showed that the sensitivity, specificity and AUC of sarcopenia in predicting the occurrence of ventricular remodelling after CABG in elderly patients with CHD were 90.0%, 47.0% and 0.772 (95% CI 0.68–0.86, P < 0.0001), respectively (Figure 1). When incorporated into the multivariable logistic regression model together with other clinical predictors, the overall model also showed good discriminative ability and satisfactory calibration, indicated by the Hosmer-Lemeshow goodness-of-fit test (χ² = 5.42, P = 0.71).
Figure 1.
ROC curves of sarcopenia predicting ventricular remodelling after coronary artery bypass grafting in elderly patients with CHD.
4. Discussion
In this study, we found that the proportion of sarcopenia in the ventricular remodelling group was significantly higher than in the non-occurrence group, and multivariate regression analysis confirmed that sarcopenia was independently associated with ventricular remodelling after CABG in elderly patients with CHD. These results are consistent with previous reports (12–14), supporting the view that sarcopenia not only increases frailty but also adversely affects cardiac structure and function. Our findings highlight the clinical importance of recognising sarcopenia as a risk factor for adverse cardiac remodelling in elderly patients undergoing CABG. Incorporating sarcopenia assessment into perioperative evaluation may help identify high-risk patients, guide nutritional and rehabilitation interventions and, ultimately, improve long-term outcomes.
Existing studies have demonstrated that conventional biomarkers such as NT-proBNP are valuable predictors of adverse outcomes after CABG. For example, elevated preoperative NT-proBNP levels have been associated with the development of postoperative left ventricular systolic dysfunction, showing moderate predictive utility (sensitivity 59%, specificity 80%) (19). A larger cohort study also identified postoperative elevations in NT-proBNP as independent predictors of postoperative heart failure, with significant ORs BioMed Central (20). Additionally, systematic reviews underscore the prognostic role of natriuretic peptides across a range of adverse cardiac outcomes following surgery ScienceDirect (21). In contrast, sarcopenia offers a distinct dimension of risk assessment. While NT-proBNP reflects acute myocardial stress and volume overload, sarcopenia integrates indicators of systemic nutritional status, muscle metabolism and frailty. In our study, sarcopenia showed a robust predictive performance (AUC = 0.862, sensitivity 87.5%, specificity 76.5%), potentially providing complementary prognostic insight beyond conventional biomarkers. Incorporating sarcopenia assessment may, therefore, refine postoperative risk stratification and guide early rehabilitation interventions. Specifically, exercise-based rehabilitation, including both resistance and aerobic training, has emerged as the most effective non-pharmacologic intervention for sarcopenia (22). Resistance training can significantly enhance muscle strength, gait speed and functional performance in older adults with sarcopenia, especially when conducted at moderate-to-high intensity (≥60% one-repetition maximum) over a sustained period (≥8–12 weeks) (23, 24). Moreover, aerobic and resistance exercise stimulate mitochondrial biogenesis, reduce inflammation, improve insulin sensitivity and upregulate PGC-1α, which may benefit not only muscle health but also cardiac remodelling processes (25, 26). These mechanistic pathways dovetail with the associations we observed between sarcopenia and adverse ventricular remodelling (e.g., via insulin resistance and systemic inflammation), suggesting that exercise rehabilitation could favourably modulate postoperative cardiac structural adaptations. Future studies should explore whether tailored, combined exercise and nutritional interventions can attenuate ventricular remodelling and improve the long-term prognoses of elderly patients with CHD undergoing CABG.
Sarcopenia plausibly links frailty, metabolic dysregulation, and inflammatory activation to adverse ventricular remodelling after CABG. Beyond a marker of aging, skeletal muscle is an endocrine organ; loss of muscle mass/strength shifts myokine balance, worsens insulin resistance, and reduces mitochondrial capacity, thereby amplifying surgical stress responses and susceptibility to remodelling (24). Within this framework, several convergent axes explain our findings: (i) Inflammation/innate immunity & endothelial dysfunction. Sarcopenia aligns with “inflammaging” monocyte activation, and impaired HDL anti-oxidative capacity. This favors endothelial activation and myocardial inflammatory signalling after surgery. The monocyte-to-HDL ratio (MHR) captures this imbalance and associates with vascular disease burden in contemporary cohorts (27). Coronary microvascular dysfunction, a consequence of endothelial injury and capillary rarefaction, promotes ischemia, diastolic stiffness, and fibrosis, all central to postoperative remodelling (28, 29). (ii) Metabolic inflexibility. Muscle loss reduces whole-body glucose disposal and mitochondrial oxidative flux, increasing ROS and lipotoxicity under peri-operative load. Exercise-responsive pathways (AMPK-PGC-1α) are down-tuned in sarcopenia; their re-engagement with training improves cardiometabolic efficiency and may counter adverse remodelling biology (30–32). (iii) Fibrosis signalling. Inflammation, oxidative stress, and RAAS activation converge on TGF-β/SMAD and matricellular nodes, driving collagen deposition and titin alterations that stiffen the ventricle (24). Our biomarkers fit this network as amplifiers rather than isolated correlates. Uric acid (UA) reflects xanthine-oxidase-mediated redox stress and is tightly linked to endothelial dysfunction and HF risk; UA-lowering contexts (e.g., SGLT2 inhibitor programs) further support a vascular-metabolic mechanism (33, 34). Lp(a) carries oxidized phospholipids, promotes vascular inflammation, and impairs fibrinolysis, sustaining afterload/ischemia-two principal drivers of post-CABG remodelling (35, 36). PAR-2 integrates protease-inflammation signalling, modulating endothelial permeability and NF-κB activity; experimental attenuation of PAR-2 reduces cardiac injury and inflammatory tone (37, 38). MHR synthesizes monocyte activity against HDL antioxidation and tracks vascular calcific/inflammatory burden (27). Consistent with this biology, sarcopenia in our cohort provided stronger discrimination for postoperative remodelling than conventional acute-stress peptides, suggesting complementarity rather than redundancy. Clinically, two strategies emerge: (1) risk identification via sarcopenia screening (sex-specific SMI cut-offs) plus inflammatory/metabolic panels (UA, Lp(a), MHR, PAR-2); and (2) mechanism-informed intervention, combining progressive resistance and aerobic training-which up-regulates PGC-1α, improves insulin sensitivity, and dampens inflammatory signalling-with targeted metabolic/vascular therapy (e.g., XO/UA pathways; emerging Lp(a)-lowering) to blunt fibrosis and adverse LV geometry after CABG.
Several previous studies (39, 40) have shown that smoking not only promotes cell aggregation and increases endothelial cell viscosity but also reduces vascular high-density lipoprotein cholesterol levels, damaging the arterial endothelium with multiple effects. The results of this study showed that smoking history was a risk factor for ventricular remodelling after CABG in elderly patients with CHD, and the reasons for this may be related to the fact that tobacco contains nicotine and coal tar, which promote endothelial hyperplasia and oxidise free radicals in the blood vessels. Patients should be instructed to strictly abstain from smoking to prevent adverse cardiac events after surgery. Diabetes mellitus is an independent risk factor for ventricular remodelling after CABG in elderly patients with CHD. This could be attributed to the fact that advanced glycosylation end-products can mediate endothelial dysfunction, leading to vascular remodelling, thereby contributing to ventricular remodelling (41). It is expected that the risk of ventricular remodelling in elderly patients with CHD who have diabetes mellitus will be reduced if they are given a treatment targeting the glycosylation end-products after coronary bypass grafting(CABG) (42). The above suggests that the occurrence of ventricular remodelling after PCI in elderly patients with CHD is the result of a multifactorial effect, and it is expected to reduce the risk of ventricular remodelling after CABG by actively controlling the patient's underlying illnesses, following a doctor's instructions for taking medication and establishing good lifestyle habits.
Several limitations of this study should be acknowledged. First, although it adopted a prospective observational design, all data were obtained from a single centre, which may limit the generalisability of the findings. Second, the relatively small sample (n = 135) may have reduced the statistical power to detect modest effects. Third, echocardiographic assessments of left ventricular volumes were performed by different sonographers at separate time points, which may introduce measurement variability despite adherence to standardised imaging protocols and blinded analysis. Fourth, the median follow-up duration was only 12 months. Ventricular remodelling is a dynamic and often slow process that can evolve over several years; therefore, our results cannot determine whether the observed sarcopenia–remodelling association persists or strengthens beyond 1 year, nor can they clarify its relationship with long-term prognosis, such as survival, or recurrent major adverse cardiovascular events. Taken together, the single-centre design, limited sample size, and short follow-up duration restrict the external validity and prognostic extrapolation of our results. Future large-scale, multicentre, prospective cohorts with extended (≥5-year) follow-up and standardised echocardiographic measurements are warranted to validate the predictive value of sarcopenia for post-CABG ventricular remodelling and to explore its influence on long-term clinical outcomes.
This study provides novel clinical insight into the integration of sarcopenia assessment into the perioperative management of elderly patients undergoing CABG. Identifying sarcopenia before surgery may help clinicians stratify patients at high risk of postoperative ventricular remodelling and poor functional recovery. Routine evaluation of muscle mass and strength, together with simple biomarkers such as PAR2 and MHR, could be incorporated into preoperative screening to refine surgical decision-making and postoperative monitoring. From a translational perspective, early recognition of sarcopenia allows for targeted prehabilitation strategies involving resistance exercise, nutritional optimisation, and metabolic control, which may improve myocardial recovery and long-term outcomes. Furthermore, integrating sarcopenia-related parameters into clinical risk models could enhance precision in predicting structural and functional cardiac adaptation after revascularisation. Collectively, these findings support a shift towards a more holistic, muscle–heart–metabolism approach for optimising outcomes in elderly patients undergoing CABG.
5. Conclusion
Sarcopenia has been identified as a risk factor for ventricular remodelling after CABG in elderly patients with CHD. Furthermore, the efficacy of sarcopenia in predicting ventricular remodelling following CABG in elderly patients with CHD was found to be high.
Funding Statement
The author(s) declare that financial support was received for the research and/or publication of this article. The 2023 Government-Funded Outstanding Talent Project of the Department of Finance zf2023034.
Footnotes
Edited by: Enyi Shi, China Medical University, China
Reviewed by: Jiayang Wang, Capital Medical University, China
Zivojin Jonjev, Institute for Cardiovascular Diseases of Vojvodina, Serbia
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.
Ethics statement
The studies involving humans were approved by This study was conducted with approval from the Ethics Committee of The First Hospital of Hebei Medical University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
HL: Conceptualization, Methodology, Writing – review & editing, Writing – original draft. FM: Formal analysis, Data curation, Writing – review & editing. YL: Writing – review & editing, Formal analysis, Data curation. ZS: Writing – review & editing, Formal analysis, Data curation. XW: Methodology, Writing – review & editing, Conceptualization, Writing – original draft.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Generative AI was used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcvm.2025.1635863/full#supplementary-material
References
- 1.Ramachandran HJ, Jiang Y, Tam WWS, Yeo TJ, Wang W. Effectiveness of home-based cardiac telerehabilitation as an alternative to phase 2 cardiac rehabilitation of coronary heart disease: a systematic review and meta-analysis. Eur J Prev Cardiol. (2022) 29(7):1017–43. 10.1093/eurjpc/zwab106 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Nichols S, McGregor G, Breckon J, Ingle L. Current insights into exercise-based cardiac rehabilitation in patients with coronary heart disease and chronic heart failure. Int J Sports Med. (2021) 42(1):19–26. 10.1055/a-1198-5573 [DOI] [PubMed] [Google Scholar]
- 3.Ilyas I, Kumar A, Adalja D, Shariff M, Desai R, Sattar Y, et al. Intracoronary brachytherapy for the treatment of recurrent drug-eluting stent in-stent restenosis: a systematic review and meta-analysis. World J Cardiol. (2021) 13(4):95–102. 10.4330/wjc.v13.i4.95 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Deo SV, Sundaram V, Sahadevan J, Selvaganesan P, Mohan SM, Rubelowsky J, et al. Outcomes of coronary artery bypass grafting in patients with heart failure with a midrange ejection fraction. J Thorac Cardiovasc Surg. (2023) 165(1):149–58. 10.1016/j.jtcvs.2021.01.035 [DOI] [PubMed] [Google Scholar]
- 5.Heuts S, Gollmann-Tepeköylü C, Denessen EJS, Olsthoorn JR, Romeo JLR, Maessen JG, et al. Cardiac troponin release following coronary artery bypass grafting: mechanisms and clinical implications. Eur Heart J. (2023) 44(2):100–12. 10.1093/eurheartj/ehac604 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Guoqing S, Cunzhi S. Correlation between myocardial work parameters and cardiac function and ventricular remodeling in patients with coronary heart disease after coronary artery bypass grafting. Med Clin Res. (2022) 39(4):630–2. [Google Scholar]
- 7.Yanyan Z, Xulan Z, Shiyang Z, Xia S, Zhang JH, Jiang F. Analysis of influencing factors of sarcopenia associated with chronic heart failure in the elderly. Chin J Phys. (2023) 25(6):914–7. [Google Scholar]
- 8.Chun HS, Lee M, Lee HA, Oh SY, Baek HJ, Moon JW, et al. Association of physical activity with risk of liver fibrosis, sarcopenia, and cardiovascular disease in nonalcoholic fatty liver disease. Clin Gastroenterol Hepatol. (2023) 21(2):358–69. 10.1016/j.cgh.2021.12.043 [DOI] [PubMed] [Google Scholar]
- 9.Luo J, le Cessie S, van Heemst D, Noordam R. Diet-derived circulating antioxidants and risk of coronary heart disease: a Mendelian randomization study. J Am Coll Cardiol. (2021) 77(1):45–54. 10.1016/j.jacc.2020.10.048 [DOI] [PubMed] [Google Scholar]
- 10.He J, Huang M, Li N, Zha L, Yuan J. Genetic association and potential mediators between sarcopenia and coronary heart disease: a bidirectional two-sample, two-step Mendelian randomization study. Nutrients. (2023) 15(13):3013. 10.3390/nu15133013 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Shen ZL, Liu Z, Zang WF, Zhang P, Zou H-B, Dong W-X, et al. Thoracic sarcopenia predicts clinical outcomes in patients undergoing coronary artery bypass grafting: a 6-year cohort study. Asian J Surg. (2023) 46(1):291–8. 10.1016/j.asjsur.2022.03.096 [DOI] [PubMed] [Google Scholar]
- 12.Suxiang X, Jianhua Y, Xinzhi L, Chen YL, Guo SG. Prevalence of sarcopenia and its impact on short-term prognosis in elderly patients with coronary heart disease. Chongqing Med. (2022) 51(11):1866–70. [Google Scholar]
- 13.Izar MCO, Fonseca FAH. Macrophages and neovascularization in in-stent neoatherosclerosis: an accelerated inflammatory phenotype by OCT with therapeutic implications. Arq Bras Cardiol. (2022) 119(6):938–9. 10.36660/abc.20220732 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Kim K, Park S, Park JH, Cho W-S, Kim H-E, Lee S-M, et al. Improved biocompatibility of intra-arterial poly-l-lactic acid stent by tantalum ion implantation: 3-month results in a swine model. J Korean Neurosurg Soc. (2021) 64(6):853–63. 10.3340/jkns.2021.0009 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. (2019) 48(1):16–31. 10.1093/ageing/afy169 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Chen LK, Woo J, Assantachai P, Auyeung T-W, Chou M-Y, Iijima K, et al. Asian working group for sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment. J Am Med Dir Assoc. (2020) 21(3):300–307.e2. 10.1016/j.jamda.2019.12.012 [DOI] [PubMed] [Google Scholar]
- 17.Rodriguez-Palomares JF, Gavara J, Ferreira-González I, Valente F, Rios C, Rodríguez-García J, et al. Prognostic value of initial left ventricular remodeling in patients with reperfused STEMI. JACC Cardiovasc Imaging. (2019) 12(12):2445–56. 10.1016/j.jcmg.2019.02.025 [DOI] [PubMed] [Google Scholar]
- 18.Legallois D, Hodzic A, Alexandre J, Dolladille C, Saloux E, Manrique A, et al. Definition of left ventricular remodelling following ST-elevation myocardial infarction: a systematic review of cardiac magnetic resonance studies in the past decade. Heart Fail Rev. (2022) 27(1):37–48. 10.1007/s10741-020-09975-3 [DOI] [PubMed] [Google Scholar]
- 19.Pérez-García TJ, Reyes-Hernández LM, Correa-Morales AM, Cuellar-Gallardo NA, Castro-Torres Y. NT-proBNP as a predictor of left ventricular systolic dysfunction after coronary artery bypass grafting. Biomed J Sci Tech Res. (2020) 30(5):23677–82. [Google Scholar]
- 20.Jiang H, Holm J, Friberg Ö, Vanky F, Vidlund M, Tajik B, et al. Utility of NT-proBNP as an objective marker of postoperative heart failure after coronary artery bypass surgery: a prospective observational study. Perioper Med (Lond). (2021) 10(1):21. 10.1186/s13741-021-00194-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Rao RA, Varghese SS, Ansari F, Rao A, Meng E, El-Diasty M. The role of natriuretic peptides in predicting adverse outcomes after cardiac surgery: an updated systematic review. Am J Cardiol. (2024) 210:16–36. 10.1016/j.amjcard.2023.09.101 [DOI] [PubMed] [Google Scholar]
- 22.Fujie S, Horii N, Kajimoto H, Yamazaki H, Inoue K, Iemitsu K, et al. Impact of resistance training and chicken intake on vascular and muscle health in elderly women. J Cachexia Sarcopenia Muscle. (2025) 16:e70153. 10.1002/jcsm.13572 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Chen N, He X, Feng Y, Ainsworth BE, Liu Y. Effects of resistance training in healthy older people with sarcopenia: a systematic review and meta-analysis of randomized controlled trials. Eur Rev Aging Phys Act. (2021) 18(1):23. 10.1186/s11556-021-00277-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Damluji AA, Alfaraidhy M, AlHajri N, Rohant NN, Kumar M, Al Malouf C, et al. Sarcopenia and cardiovascular diseases. Circulation. (2023) 147(20):1534–53. 10.1161/CIRCULATIONAHA.123.064071 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.He N, Zhang Y, Zhang L, Zhang S, Ye H. Relationship between sarcopenia and cardiovascular diseases in the elderly: an overview. Front Cardiovasc Med. (2021) 8:743710. 10.3389/fcvm.2021.743710 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Prokopidis K, Isanejad M, Akpan A, Stefil M, Tajik B, Giannos P, et al. Exercise and nutritional interventions on sarcopenia and frailty in heart failure: a narrative review of systematic reviews and meta-analyses. ESC Heart Fail. (2022) 9(5):2787–99. 10.1002/ehf2.14052 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Liu S, Miao X, Hu M, Song Z, Xie X, Sun Y, et al. Monocyte to high-density lipoprotein ratio is associated with carotid plaque: a retrospective cohort study. J Am Heart Assoc. (2025) 14(3):e037210. 10.1161/JAHA.124.037210 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Collet C, Sakai K, Mizukami T, Ohashi H, Bouisset F, Caglioni S, et al. Vascular remodeling in coronary microvascular dysfunction. JACC Cardiovasc Imaging. (2024) 17(12):1463–76. 10.1016/j.jcmg.2024.07.018 [DOI] [PubMed] [Google Scholar]
- 29.Paolisso P, Gallinoro E, Belmonte M, Bertolone DT, Bermpeis K, De Colle C, et al. Coronary microvascular dysfunction in patients with heart failure: characterization of patterns in HFrEF versus HFpEF. Circ Heart Fail. (2024) 17(1):e010805. 10.1161/CIRCHEARTFAILURE.123.010805 [DOI] [PubMed] [Google Scholar]
- 30.Zhang H, Zhang Y, Zhang J, Jia D. Exercise alleviates cardiovascular diseases by improving mitochondrial homeostasis. J Am Heart Assoc. (2024) 13(19):e036555. 10.1161/JAHA.124.036555 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Shah IA, Ishaq S, Lee SD, Wu BT. Effects of exercise training on cardiac mitochondrial functions in diabetic heart: a systematic review. Int J Mol Sci. (2024) 26(1):8. 10.3390/ijms26010008 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Hastings MH, Castro C, Freeman R, Abdul Kadir A, Lerchenmüller C, Li H, et al. Intrinsic and extrinsic contributors to the cardiac benefits of exercise. JACC Basic Transl Sci. (2023) 9(4):535–52. 10.1016/j.jacbts.2023.07.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Doehner W, Anker SD, Butler J, Zannad F, Filippatos G, Coats AJS, et al. Uric acid and SGLT2 inhibition with empagliflozin in heart failure with preserved ejection fraction: the EMPEROR-preserved trial. JACC Heart Fail. (2024) 12(12):2057–70. 10.1016/j.jchf.2024.08.020 [DOI] [PubMed] [Google Scholar]
- 34.Ndrepepa G. Uric acid and cardiovascular disease—recent evidence on the association and underlying mechanisms. J Lab Precis Med. (2025) 10:8. 10.21037/jlpm-25-8 [DOI] [Google Scholar]
- 35.Tsimikas S. Lipoprotein(a) in the year 2024: a look back and a look ahead. Arterioscler Thromb Vasc Biol. (2024) 44(7):1485–90. 10.1161/ATVBAHA.124.319483 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Gilliland TC, Liu Y, Mohebi R, Miksenas H, Haidermota S, Wong M, et al. Lipoprotein(a), oxidized phospholipids, and coronary artery disease severity and outcomes. J Am Coll Cardiol. (2023) 81(18):1780–92. 10.1016/j.jacc.2023.02.050 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Villano G, Pontisso P. Protease activated receptor 2 as a novel druggable target for the treatment of metabolic dysfunction-associated fatty liver disease and cancer. Front Immunol. (2024) 15:1397441. 10.3389/fimmu.2024.1397441 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Meyer Zu Schwabedissen A, Vergarajauregui S, Bertog M, Amann K, Engel FB, Daniel C. Protease-activated receptor 2 deficient mice develop less angiotensin II induced left ventricular hypertrophy but more cardiac fibrosis. PLoS One. (2024) 19(12):e0310095. 10.1371/journal.pone.0310095 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Balasubramanian P, Kernan WN, Sheth KN, Ofstad AP, Rosenstock J, Wanner C, et al. Baseline cardiovascular risk factor control in patients with type 2 diabetes and coronary disease versus stroke: secondary analysis of cardiovascular outcome trials. Stroke. (2023) 54(8):2013–21. 10.1161/STROKEAHA.122.042053 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Weili M, Qifeng L, Fengmei B, Cai Y, Zhang FM. NLR and MHR and their combination in the diagnosis of in-stent restenosis after percutaneous coronary intervention in patients with coronary heart disease. Guangxi Med J. (2021) 43(8):913–6. [Google Scholar]
- 41.Podadera-Herreros A, Alcala-Diaz JF, Gutierrez-Mariscal FM, Jimenez-Torres J, Cruz-Ares Sdl, Arenas-de Larriva AP, et al. Long-term consumption of a Mediterranean diet or a low-fat diet on kidney function in coronary heart disease patients: the CORDIOPREV randomized controlled trial. Clin Nutr. (2022) 41(2):552–9. 10.1016/j.clnu.2021.12.041 [DOI] [PubMed] [Google Scholar]
- 42.Melita H, Manolis AA, Manolis TA, Manolis AS. Lipoprotein(a) and cardiovascular disease: a missing link for premature atherosclerotic heart disease and/or residual risk. J Cardiovasc Pharmacol. (2022) 79(1):18–35. 10.1097/FJC.0000000000001160 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.