Acute myocardial infarction (AMI) is a major contributor to death globally and imposes significant costs related to medical treatment and work-related disabilities (1). Even though percutaneous coronary intervention (PCI) represents a sophisticated advanced therapy for AMI, this disease remains a prevalent and deadly form of cardiovascular illness, continuing to pose significant challenges to public health (2, 3). To enhance outcomes for AMI patients following PCI, it is crucial to further recognize those at heightened risk and strive to minimize the occurrence of negative events.

Malnutrition has been consistently identified by various studies as an independent risk factor for cardiovascular diseases, involving coronary artery disease (CAD), heart failure, cardiomyopathy, non-valvular atrial fibrillation (AF) and peripheral vascular disease (4–9). It is significantly associated with elevated all-cause mortality and increased incidence of major adverse cardiovascular events (MACE). Previous studies have suggested that the elevated mortality risk of AMI associated with malnutrition may be attributable to inflammatory mechanisms (10, 11). Inflammation can impair endothelial cell function, increase lipoprotein permeability, and facilitate the progression of atherosclerosis. Besides, inflammation may further exacerbate malnutrition (12, 13). Stenvinkel et al. reported that inflammation and malnutrition are closely linked to arteriosclerosis, and they have referenced Malnutrition-Inflammation-Atherosclerosis (MIA) syndrome (14). Currently, the American Heart Association (AHA) has demonstrated through the implementation of nutritional intervention measures that such approaches can reduce high-sensitivity C-reactive protein (CRP) level, thereby effectively improving patient prognosis (15). Consequently, timely and accurate assessment of patients’ nutritional status holds critical importance in clinical practice. With the aid of multiple nutritional assessment tools, the malnutrition status of patients can not only be quantified but also serve as an auxiliary factor for risk stratification. It provides clinicians with a more efficient approach to address patients’ malnutrition status. The Controlling Nutritional Status (CONUT) score is a nutritional assessment tool which comprises serum albumin, total cholesterol and lymphocyte counts, and its simplicity and effectiveness have been acknowledged (16–18). Nonetheless, the research surrounding the CONUT score, which serves to forecast the outcomes for patients experiencing AMI and undergoing PCI, remains sparse.

Accordingly, we intend to illustrate the incidence and long-term outcomes related to malnutrition in AMI patients following PCI while utilizing the CONUT score. Furthermore, the study aims to explore if the CONUT score can enhance the prognostic capabilities of the GRACE risk score concerning all-cause mortality.

In the analysis conducted retrospectively, we observed that (1) patients suffering from AMI frequently experience malnutritional conditions, with our study revealing that nearly 48.5% of the patients experienced malnutrition, with 4.8% experienced moderate and severe malnutrition. (2) Malnutrition is an independent predictor of all-cause mortality in patients who underwent PCI after AMI, and the CONUT score, as a nutritional assessment tool, can assist in risk stratification. (3) The CONUT score can increase the prognostic value of the GRACE risk score for predicting all-cause mortality in AMI patients who underwent PCI. These aspects underscore the critical need for mitigating malnutrition risks and stress the importance of prompt intervention.

The incidence of malnutrition is very common among patients with AMI. Huang Y et al. found that malnutrition accounted for 53.8% in a cohort of 1180 AMI patients based on the PNI score (22). Basta G et al. showed that 55% of patients were malnourished in their evaluation of the nutritional status within a STEMI cohort (23). In our study, the prevalence of malnutrition was comparable to that reported in prior research. However, Wada et al. found that a significant proportion, approximately 20% of patients with coronary artery disease who underwent PCI were identified as being at high risk for malnutrition (24). Another study examined malnutrition prevalence in a group of 207 patients diagnosed with NSTEMI, uncovering a rate of 24.4% (25). The incidence of malnutrition in both studies was lower compared to our study. The potential reasons for this distinction may include different study population and different scoring system compared to our study.

Within our study cohort, it was observed that malnutrition independently contributed to all-cause mortality among patients who underwent PCI after AMI. Various previous investigations have also shown that malnutrition acts as an independent predictor of AMI when evaluated using different assessment methods. Li M et al. indicated that Prognostic Nutritional Index (PNI) was an independent predictor of long-term prognosis in elderly STEMI patients (26). Additionally, another study employed the Geriatric Nutrition Risk Index (GNRI) to gauge nutritional status, highlighting a notable connection between GNRI and unfavorable outcomes in AMI patients following PCI (27). Few studies have utilized the CONUT score to assess the relationship between malnutrition and the prognosis of patients who underwent PCI after AMI. PNI, GNRI, and CONUT score are widely recognized and commonly utilized nutritional assessment tools (28). We selected CONUT as the nutritional assessment tool for our study based on the findings of Raposeiras Roubín S et al., who evaluated the predictive value of PNI, GNRI, and CONUT for all-cause mortality and MACE in ACS patients. Their results suggested that the predictive capability of the CONUT score ranked the highest (29).

The GRACE risk score exhibits superior predictive performance for both in-hospital mortality and one-year mortality in patients with AMI (30). As AMI management and catheter devices have changed considerably since the establishment of the GRACE score, it is therefore important to develop a new scoring model for prospective risk stratification that is suitable for the current era of AMI management (31). Our research found that CONUT can improve the predictive efficiency of GRACE risk score. This may can be more effectively aid in the risk stratification of patients following PCI after AMI.

The pathophysiology of the relationship between high CONUT scores and poor clinical outcomes in AMI patients is not clearly known. The CONUT score is calculated based on the serum albumin, total cholesterol level and the lymphocyte count in the peripheral blood. The serum albumin has been confirmed by multiple studies as an indicator related to inflammation (32, 33). The activity of proinflammatory cytokines is contributed to the decrease in serum albumin levels (10, 34). On the other hand, atherosclerosis is increasingly recognized as a chronic inflammatory disease with an autoimmune component. A decrease in lymphocytes may cause immunosuppression in the patient, which can influence cardiac remodeling process after AMI and lead to poor prognosis (35–38). Meanwhile, the immune system primarily mediates its immune response by recognizing LDL antibodies, thereby generating oxidized LDL and triggering inflammation in the arterial wall (39). The above-mentioned evidence supports that the rationality of the CONUT score in assessing the prognosis of AMI patients, and indicates that there is an interaction and mutual promotion relationship between malnutrition, inflammation and immune response. All these findings strongly underscore the importance for physicians to incorporate malnutrition identification into their daily practice. Especially, patients with AMI are generally elderly, fragile patients with comorbidities. The CONUT score will provide important guidance for subsequent nutritional intervention measures. Shah B et al. indicates that dietary interventions can reduce high-sensitivity CRP level, which may have a beneficial effect on the prognosis of coronary heart disease (15). In the future, more well-designed experiments should be conducted to evaluate the specific effects of nutritional interventions on the prognosis of patients who underwent PCI after AMI.

Our study is subject to several limitations. Firstly, it was a single-center, retrospective study. All of our patients were of Chinese ethnicity, which may not reflect the prognosis observed in other ethnic groups, thereby limiting the general applicability of our findings. Secondly, we only utilized simple screening tools to assess nutritional status, without conducting direct comparisons with other well-established nutritional assessment tools, such as the Subjective Global Assessment and the Mini Nutritional Assessment. Furthermore, since the assessment of nutrition was carried out at just one point in time, it limited our ability to track the evolving nutritional conditions of the patients over the study period. Assessing long-term clinical outcomes using information collected at just one moment carries fundamental limitations. Besides, regarding medical therapy, we only conducted statistical analysis on the medication treatment of patients after discharge, without covering the medication situation before admission (such as the application of statins). This limitation may have a certain impact on the accuracy of cholesterol levels in the baseline data. Ultimately, while numerous clinically significant factors were incorporated in the multivariate evaluation, it is likely that unaccounted confounding variables still exist.

Malnutrition is prevalent among patients with AMI and is significantly associated with an increased incidence of all-cause mortality. The CONUT score, serving as a nutritional assessment tool, plays a key role in assisting with risk categorization and forecasting patients’ poor prognosis. The predictive capability of the GRACE risk score regarding all-cause mortality can be improved by incorporating the CONUT score. Early screening of malnutrition using the CONUT score can provide a foundation for guiding subsequent clinical nutritional interventions.