The predictive value of the pre-PCI prognostic nutritional index combined with the geriatric nutritional risk index for one-year outcomes in patients with chronic total occlusion

Aim: Chronic total occlusion (CTO) is associated with high rates of major adverse cardiovascular and cerebrovascular events (MACCEs) after percutaneous coronary intervention (PCI). Nutritional and inflammatory status are increasingly recognized as key prognostic factors. This study aimed to evaluate the predictive value of the prognostic nutritional index (PNI) combined with the geriatric nutritional risk index (GNRI) for MACCEs in CTO patients undergoing PCI.

Methods: A total of 395 CTO patients from Northeast China who were treated with PCI at Liaoning Provincial People’s Hospital between February 2019 and December 2023 were retrospectively analyzed. Baseline clinical, laboratory, and procedural data were collected. The PNI and GNRI scores were calculated based on pre-PCI laboratory test results, and patients were followed for 12 months to monitor the occurrence of MACCEs. Independent predictors were identified using logistic regression analysis, and the predictive performance of three models was evaluated using ROC curves, C-statistics, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and Kaplan–Meier survival analysis.

Results: During follow-up, 125 patients (31.6%) experienced MACCEs. Both PNI-GNRI were independent predictors of MACCEs risk. Adding PNI to the baseline risk model increased the C-statistic from 0.696 to 0.770 (p < 0.001). Incorporating GNRI further increased it to 0.826 (p < 0.001), with significant improvements in NRI (0.308) and IDI (0.207). Kaplan–Meier analysis demonstrated that patients with low PNI or GNRI scores had significantly higher cumulative incidence of MACCEs. Subgroup analyses confirmed the stability of these associations across various patient strata.

Conclusion: Both PNI-GNRI are independent predictors of MACCEs, and their combined model provides superior prognostic stratification for CTO patients compared with traditional risk models, particularly in elderly patients. Comprehensive assessment of nutritional and inflammatory status enables precise perioperative risk stratification. It also offers guidance for individualized management, nutritional interventions, and long-term rehabilitation.

1 Introduction

Coronary heart disease (CHD) is a common cardiovascular disorder caused by myocardial ischemia, hypoxia, and/or necrosis. Its primary pathological mechanism is the development of coronary atherosclerosis, leading to vascular narrowing or even complete occlusion. As one of the most prevalent types of cardiovascular disease, CHD affects millions of people worldwide (1, 2).

Although the mortality of coronary artery disease (CAD) has declined in recent decades, it remains a leading cause of death worldwide and imposes a major public health and economic burden (3). Epidemiological data indicate that CHD accounts for approximately 32.7% of global heart disease and 1.7% of the total disease burden (4). Chronic total occlusion (CTO) is detected in 15–30% of patients undergoing coronary angiography and is characterized by occlusion lasting over 3 months with insufficient collateral circulation, leading to persistent ischemia and angina (5). Compared with non-CTO lesions, CTO is associated with lower success rates of percutaneous coronary intervention (PCI) and higher risks of adverse cardiovascular and cerebrovascular events (6). In recent years, growing evidence has demonstrated that nutritional imbalance is closely associated with poor clinical outcomes in patients with cardiovascular diseases (7, 8). Adequate nutrition not only helps maintain a good quality of life but also reduces the risk of chronic diseases (9). Therefore, systematic nutritional screening and intervention in patients with cardiovascular diseases are considered key strategies for preventing cachexia (10).

The objective quantification of nutritional status has long been a challenge in clinical research. The prognostic nutritional index (PNI), one of the commonly used tools, is calculated based on serum albumin concentration and lymphocyte count, thereby providing a comprehensive assessment of nutritional and immune status. Originally developed for nutritional evaluation in patients undergoing gastrointestinal surgery, PNI has increasingly been investigated for prognostic prediction in patients with CAD. Current evidence suggests that malnutrition not only independently predicts mortality and major adverse cardiovascular and cerebrovascular events (MACCEs) in CAD patients (11), but that a low PNI value is also closely associated with the presence of multivessel coronary lesions (12). However, the effectiveness of PNI in predicting clinical outcomes remains inconsistent across studies, and available evidence is still limited (13). Another nutritional assessment indicator is the geriatric nutritional risk index (GNRI), proposed by Bouillanne et al. (14), which is calculated using serum albumin levels and the ratio of actual to ideal body weight. GNRI is a simple and reliable scoring method for assessing nutritional risk. It can predict morbidity and mortality in elderly patients and has been widely used to evaluate the association between nutritional status and adverse outcomes in diverse populations. Previous studies have demonstrated that malnutrition identified by GNRI at admission is an independent predictor of MACCEs in patients with CTO undergoing PCI (6, 15). Nevertheless, evidence regarding the relationship between malnutrition and adverse cardiovascular and cerebrovascular events in CTO patients after PCI remains scarce.

Therefore, the present study aims to evaluate the predictive value of GNRI combined with PNI for MACCEs in CTO patients treated with PCI. The goal is to provide new perspectives and evidence-based insights for risk stratification and prognosis management in this population.

2 Methods

2.1 Patient population

From February 2019 to December 2023, a total of 440 patients who were hospitalized at Liaoning Provincial People’s Hospital for the first time and diagnosed with at least one CTO in a major coronary artery (left anterior descending artery, left circumflex artery, or right coronary artery) by coronary angiography were screened. According to the exclusion criteria, 15 patients (3.4%) were excluded, and 30 patients (6.8%) were lost to follow-up. Ultimately, 395 patients were included in this study (Figure 1). As a retrospective analysis, it was approved by the Ethics Committee of Liaoning Provincial People’s Hospital (Approval No.2024-K063).

Figure 1

Figure 1. Flow diagram of participant selection.

2.2 Inclusion criteria

(1) Age ≥18 years; (2) Meeting the diagnostic criteria for CTO: complete interruption of antegrade flow (TIMI grade 0) on coronary angiography with an occlusion duration of ≥3 months; (3) Undergoing PCI with successful revascularization of the occluded vessel; (4) Availability of complete clinical data.

2.3 Exclusion criteria

(1) Contraindications to PCI or contrast agent administration; (2) History of acute myocardial infarction within 1 month, or emergency coronary angiography/stent implantation during the acute phase; (3) Congenital coronary artery anomalies; (4) Connective tissue diseases involving the coronary arteries; (5) Other coexisting cardiac diseases, such as congenital heart disease, severe valvular disease, cardiomyopathy, or cor pulmonale; (6) Previous intravenous thrombolysis, thrombectomy, or coronary artery bypass grafting (CABG); (7) Severe hepatic or renal dysfunction; (8) Severe infectious diseases; (9) Presence of malignancy or systemic immunodeficiency disorders; (10) Patients with nephrotic syndrome, hypersensitivity reactions, hematologic malignancies, systemic lupus erythematosus, or other diseases known to significantly affect albumin or lymphocyte levels.

2.4 Follow-up

All patients were followed up at 6 and 12 months after PCI, through multiple approaches including but not limited to telephone interviews and outpatient visits. Information on adverse events was obtained either by telephone contact with patients or their family members, or during outpatient follow-up visits. During the follow-up, 30 patients (6.8%) were lost, and a total of 395 patients successfully completed follow-up.

2.5 Study endpoints

Based on the occurrence of MACCEs within 12 months after PCI, patients were divided into two groups: the MACCEs group and the non-MACCEs group. Demographic characteristics, laboratory parameters, imaging findings, coronary angiography results, GNRI scores, and PNI scores were compared between the two groups.

2.6 Definitions

GNRI = [1.489 × albumin (g/L)] + [41.7 × (weight/ideal body weight WLo)].

Ideal body weight (WLo) was calculated using the Lorentz formula:

Male: H-100-[(H-150)/4]; Female: H-100-[(H-150)/2.5] (14).

PNI = 10 × serum albumin (g/L) + 5 × total lymphocyte count (10⁹/L) (16).

2.7 Statistical analysis

All statistical analyses were conducted using SPSS version 26.0 and R version 4.3.2. Continuous variables were presented as mean ± standard deviation, and categorical variables as counts and percentages. Between-group comparisons were performed using the independent-samples t-test or the non-parametric rank-sum test for continuous variables, and the chi-square test for categorical variables. Univariate logistic regression was initially employed to identify potential predictors of MACCEs. Variables with p < 0.05 were subsequently entered into a multivariate logistic regression model, with results expressed as odds ratios (ORs) and 95% confidence intervals (CIs). Model discrimination was assessed by receiver operating characteristic (ROC) curve analysis and the C-statistic, while subgroup effect sizes were illustrated using forest plots. Furthermore, the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated to evaluate the incremental predictive value and clinical utility of the novel model. Survival curves were generated with the Kaplan–Meier method and compared using the log-rank test. A two-sided p < 0.05 was considered statistically significant.

3 Results

3.1 Baseline characteristics of the study population

A total of 395 patients meeting the inclusion criteria were enrolled, with a median follow-up of 12 months. Among them, 125 patients (31.65%) experienced MACCEs, while 270 patients (68.35%) did not. The baseline characteristics of the overall cohort are summarized in Table 1. Compared with the non-MACCEs group, patients in the MACCEs group showed statistically significant differences in the prevalence of diabetes, hypertension, and current smoking, as well as in lymphocyte count, serum albumin, low-density lipoprotein cholesterol (LDL-C), GNRI score, PNI score, the number of CTO-involved vessels, number of stents implanted, and total stent length (all p < 0.05). Overall, patients in the MACCEs group exhibited a higher prevalence of cardiovascular metabolic and lifestyle-related risk factors, particularly diabetes, hypertension, and smoking, compared with those without MACCEs (all p < 0.05). In addition, these patients demonstrated poorer lipid control, as evidenced by elevated LDL-C levels, indicating a greater burden of dyslipidemia. With respect to nutritional and inflammatory parameters, the MACCEs group showed significantly higher lymphocyte counts but lower GNRI and PNI scores, as well as reduced serum albumin levels, suggesting a state of impaired nutritional status and systemic inflammatory response. Regarding PCI-related characteristics, patients who experienced MACCEs had a greater number of CTO-involved vessels, received more stents, and had longer total stent lengths than those in the non-MACCEs group (all p < 0.05).

Table 1

Table 1. Baseline clinical characteristics.

3.2 Risk factor analysis

Univariate and multivariate logistic regression analyses were performed to identify predictors of MACCEs in this cohort (Table 2). The results demonstrated that diabetes, hypertension, current smoking, LDL-C, GNRI score, PNI score, number of CTO-involved vessels, number of stents implanted, and total stent length were all significant risk factors for MACCEs (all p < 0.05). Detailed findings are as follows: A history of diabetes was significantly associated with an increased risk of the primary endpoint in both univariate (OR = 1.729, 95%CI: 1.126–2.655, p = 0.012) and multivariate analyses (OR = 2.029, 95%CI: 1.199–3.433, p = 0.008). Hypertension was also strongly correlated with an increased risk of MACCEs in univariate (OR = 2.053, 95%CI: 1.279–3.295, p = 0.003) and multivariate analyses (OR = 2.151, 95%CI: 1.217–3.803, p = 0.008). Elevated LDL-C was significantly associated with MACCEs in univariate (OR = 1.721, 95%CI: 1.332–2.224, p < 0.001) and multivariate analyses (OR = 1.594, 95%CI: 1.177–2.159, p = 0.003). Lower GNRI scores were strongly predictive of MACCEs in univariate (OR = 0.852, 95%CI: 0.819–0.887, p < 0.001) and multivariate analyses (OR = 0.802, 95%CI: 0.749–0.860, p < 0.001). Similarly, PNI was significantly associated with the primary endpoint, as shown in univariate (OR = 0.872, 95%CI: 0.833–0.912, p < 0.001) and multivariate analyses (OR = 1.091, 95%CI: 1.005–1.185, p = 0.037). Although the adjusted OR for PNI in the multivariate analysis is slightly above 1, this may reflect the influence of multicollinearity and adjustment for confounding factors. The overall results from the univariate analysis, as well as subsequent ROC and Kaplan–Meier survival analyses, consistently indicate that higher PNI is associated with better outcomes. Moreover, the number of CTO-involved vessels was significantly correlated with MACCEs occurrence in univariate (OR = 2.472, 95%CI: 1.368–4.470, p = 0.003) and multivariate analyses (OR = 2.362, 95%CI: 1.101–5.068, p = 0.027).

Table 2

Table 2. Univariate and multivariate logistic regression analysis.

3.3 Evaluation of PNI-GNRI in MACCEs risk prediction models

The synergistic effect of combining the PNI-GNRI in predicting MACCEs after PCI in CTO patients is shown in Table 3 and Figure 2. Compared with the baseline model including established risk factors (Model 1), the addition of PNI (Model 2) improved the C-statistic from 0.696 [95% CI (0.643–0.750), p < 0.001] to 0.770 [95% CI (0.723–0.818), p < 0.001]. Furthermore, incorporating both PNI-GNRI (Model 3) significantly increased the C-statistic from 0.696 [95% CI (0.643–0.750), p < 0.001] to 0.826 [95% CI (0.783–0.868), p < 0.001], demonstrating the strongest incremental effect. In addition, this combined model markedly improved the NRI by 0.308 [95% CI (0.239–0.376), p < 0.001] and the IDI by 0.207 [95% CI (0.163–0.251), p < 0.001]. A higher NRI indicated that incorporating nutritional indices improved the accuracy of patient risk reclassification for MACCEs, while an increased IDI reflected enhanced overall discrimination and predictive performance of the model. These results demonstrate that adding nutritional parameters meaningfully strengthened the model’s clinical predictive value.

Table 3

Table 3. Evaluation of predictive models for MACCEs.

Figure 2

Figure 2. ROC curve analysis of the three models.

3.4 Subgroup analyses

Subgroup analyses demonstrated that the relationship between the PNI and the risk of MACCEs remained consistent across various patient strata, including age, sex, BMI, diabetes mellitus, hypertension, current smoking status, LVEF, LDL-C, Hb, WBC, PLT, ALT, AST, FPG, Cr, number of CTO vessels, number of stents implanted, overall stent length, total procedural time, and contrast volume, with no significant interactions observed (all P for interaction > 0.05) (Figure 3). Subgroup analyses demonstrated that the relationship between the GNRI and the risk of MACCEs remained consistent across various patient strata, including age, sex, BMI, diabetes mellitus, hypertension, current smoking status, LVEF, LDL-C, Hb, WBC, PLT, ALT, AST, FPG, Cr, number of CTO vessels, number of stents implanted, overall stent length, and contrast volume, with no significant interactions observed (all P for interaction > 0.05) (Figure 4). Notably, a significant interaction was observed between GNRI and procedural time (P for interaction = 0.049), suggesting that procedural time may modify the prognostic impact of GNRI.

Figure 3

Figure 3. Subgroup analysis between the PNI and MACCEs across various subgroups.

Figure 4

Figure 4. Subgroup analysis between the GRNI and MACCEs across various subgroups.

3.5 Predictive value of the PNI-GNRI for the risk of MACCEs

The Kaplan–Meier analysis demonstrated that patients with a low PNI (≤51.35) exhibited a significantly higher cumulative incidence of MACCEs compared with those with higher PNI levels (log-rank test, p < 0.001) (Table 4 and Figure 5). Similarly, patients with a low GNRI (≤102.70) experienced a markedly increased cumulative incidence of MACCEs following PCI (log-rank test, p < 0.001) (Table 4 and Figure 6). Overall, the risk of MACCEs was substantially elevated in both the low PNI and low GNRI groups. The incidence of MACCEs in the high PNI group was reduced by 47.1% compared with the low PNI group (HR = 0.529, 95% CI: 0.330–0.847, p = 0.008). Similarly, patients in the high GNRI group experienced a 65.4% reduction in MACCEs incidence compared with the low GNRI group (HR = 0.346, 95% CI: 0.177–0.676, p = 0.002).

Table 4

Table 4. Clinical outcomes on follow-up after one-year.

Figure 5

Figure 5. The PNI and risk: Kaplan–Meier curves for the incidences of MACCEs. The optimal cut-off value of PNI (≤51.35) was determined using ROC curve analysis by maximizing the Youden index (sensitivity + specificity − 1).

Figure 6

Figure 6. The PNI and risk: Kaplan–Meier curves for the incidences of MACCEs. The optimal cut-off value of GNRI (≤102.70) was determined using ROC curve analysis by maximizing the Youden index (sensitivity + specificity − 1).

4 Discussion

The CTO represents one of the most challenging lesion types in coronary intervention, characterized by a pathological process that is prolonged, complex, and systemic in nature (17, 18). Although remarkable advances have been made in catheter-based and imaging techniques in recent years, substantial differences in long-term outcomes among CTO patients persist (19, 20), indicating that anatomical complexity alone is insufficient to comprehensively capture their clinical risk (21). Increasing evidence suggests that systemic nutritional and inflammatory status plays a pivotal role in determining the long-term prognosis of CTO patients (22, 23). In the present study, we found that the combined PNI-GNRI prediction model demonstrated superior performance in assessing the risk of MACCEs within 1 year after PCI in CTO patients. These findings highlight that nutrition-inflammation imbalance may be one of the key mechanisms influencing their prognosis. Consistent with previous studies (24–26), both PNI and GNRI were significantly associated with MACCEs; however, our findings provide additional insight into their complementary roles. PNI, which incorporates serum albumin levels and lymphocyte counts, primarily reflects short-term inflammatory and immune status. This makes it particularly sensitive to acute physiological stress and inflammatory responses that may predispose patients to early cardiovascular events (27). In contrast, GNRI, calculated from serum albumin and the ratio of actual to ideal body weight, serves as a marker of long-term nutritional reserves, reflecting chronic nutritional status and overall physiological resilience (28). The distinction between these indices suggests that PNI and GNRI capture different yet complementary aspects of patient health, explaining why their combined use enhances risk stratification beyond what either measure provides alone. Compared with previous literature, our study emphasizes not only the individual predictive value of each index but also their synergistic utility in clinical practice. CTO lesions are often accompanied by chronic myocardial ischemia, myocardial remodeling, and compensatory development of collateral circulation, with a complex disease course that may persist for months or even years (29, 30). During this process, inflammation and nutritional imbalance are closely interconnected. Hypoalbuminemia is not only a direct manifestation of malnutrition but also a consequence of chronic inflammatory activation (31). Pro-inflammatory cytokines such as IL-6 and TNF-α suppress albumin synthesis, resulting in reduced plasma oncotic pressure and impaired endothelial barrier function, thereby exacerbating edema and hemodynamic instability (32). During inflammation, hepatic protein synthesis, including that of albumin, may be impaired, thereby favoring the production of pro-inflammatory cytokines (33). Malnutrition leads to impaired immune cell function, including dysfunction of macrophages and T lymphocytes. This hampers the effective clearance of cellular debris and inflammatory mediators in the infarcted myocardium, thereby prolonging the inflammatory response (34). After myocardial injury, the innate immune system initiates an inflammatory cascade that activates monocytes, triggers neutrophil-mediated inflammation, and recruits macrophage and lymphocyte subsets to support tissue repair and restore immune balance. These adaptive responses enable the heart to transiently cope with increased stress (35). Immunosuppression also increases susceptibility to infections, adding further systemic burden (36). Prolonged nutritional–immune imbalance compromises vascular repair mechanisms, heightening the risks of intravascular thrombosis and in-stent restenosis. Inflammatory mediators activate signaling pathways such as NF-κB and STAT3, inhibiting endothelial cell proliferation and migration and impairing angiogenesis (37); sustained activation of the IL-6/STAT3 axis suppresses vascular endothelial growth factor (VEGF) signaling, thereby hindering collateral vessel formation and resulting in insufficient revascularization (38). Moreover, the GNRI not only reflects serum albumin levels but also incorporates the ratio of actual to ideal body weight, capturing long-term nutritional depletion and muscle loss (39). Muscle atrophy can result in metabolic and endocrine disturbances as well as impaired muscle contractility, and is often accompanied by hypoalbuminemia, which in turn affects systemic metabolism, immune function, and inflammatory responses (40). In addition, the lack of muscle contraction reduces the secretion of anti-inflammatory myokines (such as IL-10 and IL-15), while visceral fat accumulation promotes macrophage infiltration and increases pro-inflammatory cytokines, including TNF-α and IL-1β, thereby exacerbating vascular endothelial injury (41). Moreover, the loss of protein triggers compensatory hepatic protein synthesis, leading to an imbalance among coagulation, anticoagulation, and fibrinolytic processes (42). Under the combined influence of low protein and low body weight, myocardial tolerance to ischemia–reperfusion injury declines, and mitochondrial dysfunction becomes more pronounced. This further deranges energy metabolism and ultimately limits cardiac functional recovery. Specifically, mitochondrial calcium facilitates the activation of Krebs cycle dehydrogenases, thereby supporting energy metabolic homeostasis and cardiac function. However, this process is disrupted in heart failure, where impaired mitochondrial calcium uptake leads to NADPH oxidation, excessive ROS generation, and ultimately cardiac dysfunction (43). Collectively, these mechanisms demonstrate that nutrition–inflammation imbalance is not merely a marker of systemic status but also directly participates in the entire process of CTO revascularization through cellular signaling pathways, exerting adverse effects from vessel recanalization to postoperative recovery.

From a clinical perspective, a single indicator is often insufficient to capture the prognostic risk of patients. While the PNI primarily reflects immune–inflammatory status, the GNRI emphasizes long-term nutritional status, their combination provides complementary information, improving sensitivity and specificity in risk stratification. Our findings demonstrate that the combined PNI–GNRI model effectively predicts MACCEs, particularly in elderly CTO patients, who are prone to frailty due to muscle loss, endocrine decline, and chronic low-grade inflammation (44). By capturing systemic status across different temporal dimensions, PNI–GNRI can guide precise perioperative risk assessment and inform postoperative rehabilitation and long-term management. In practice, patients could be stratified into low-, intermediate-, and high-risk groups, with tailored nutritional support, anti-inflammatory therapy, and exercise-based rehabilitation. Dynamic monitoring of PNI–GNRI trajectories may further enhance clinical utility: improvements indicate restoration of metabolic and immune balance, while persistent deficits signal the need for intensified interventions (45). This integrated approach highlights the translational potential of PNI–GNRI in advancing individualized, precision care for CTO patients.

Although this study confirmed the clinical value of the combined PNI-GNRI predictive model in patients with CTO, several issues remain unresolved. First, most investigations on the relationship between nutritional inflammation and CTO prognosis have been limited to clinical observations, with insufficient exploration of the underlying molecular mechanisms. Future research should integrate metabolomics, proteomics, and related approaches to comprehensively elucidate how disturbances in nutritional–inflammatory balance influence cardiovascular outcomes through immunometabolic pathways. Second, PNI-GNRI are static indicators and thus cannot capture the dynamic changes during disease progression. The development of dynamic monitoring strategies, potentially incorporating wearable devices and artificial intelligence, may allow for more accurate prediction of MACCEs risk. Third, interventional studies remain lacking. To date, no randomized controlled trials have demonstrated that improving PNI or GNRI can directly reduce the incidence of MACCEs in CTO patients. Large-scale, multicenter, prospective studies are therefore warranted to assess the causal relationships between PNI, GNRI, and clinical outcomes. If validated, these indices may serve not only as prognostic tools but also as therapeutic targets.

4.1 Limitation

First, this study was a single-center, retrospective analysis, which inevitably carries the risk of selection bias and may limit the generalizability of the findings. Second, the follow-up period was restricted to only 1 year, making it impossible to assess the long-term prognostic value of PNI-GNRI in patients with CTO. Third, the sample size was relatively modest, with only 395 patients included, which may reduce the statistical power of the study. Future investigations should aim to recruit larger cohorts with extended follow-up durations and adopt multicenter, prospective study designs to further validate our results. Moreover, such studies would be essential to determine the applicability and robustness of PNI-GNRI across diverse populations and clinical settings.

5 Conclusion

This study demonstrates that both PNI-GNRI are independent predictors of MACCEs in patients with CTO undergoing PCI. Compared with traditional risk models, the combined PNI–GNRI model exhibits superior sensitivity and specificity for risk stratification, particularly among patients. Comprehensive assessment of nutritional and inflammatory status not only facilitates precise perioperative risk evaluation but also provides a foundation for individualized interventions and long-term recovery planning. Future multicenter, prospective studies are needed to confirm these findings and assess their applicability across diverse patient populations.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by the Ethics Committee of Liaoning Provincial People’s Hospital. 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. The manuscript presents research on animals that do not require ethical approval for their study.

Author contributions

ZB: Writing – original draft, Formal analysis, Methodology, Data curation, Investigation. SY: Methodology, Data curation, Investigation, Conceptualization, Writing – original draft, Resources, Formal analysis, Validation. BH: Conceptualization, Investigation, Writing – original draft, Project administration, Data curation. ZJ: Conceptualization, Data curation, Writing – original draft. LY: Investigation, Writing – review & editing, Formal analysis. LF: Writing – original draft, Formal analysis, Visualization. YQ: Writing – review & editing, Supervision, Methodology. ZY: Writing – original draft, Formal analysis, Data curation. LB: Validation, Supervision, Funding acquisition, Writing – review & editing, Conceptualization, Resources. DY: Investigation, Resources, Supervision, Validation, Writing – review & editing, Methodology. ZX: Validation, Methodology, Resources, Writing – review & editing, Supervision, Project administration.

Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

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.

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Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2025.1707981/full#supplementary-material

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