STRENGTHS AND LIMITATIONS OF THIS STUDY
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The main strength of this study was the use of restricted cubic spline to display the J-shaped relationship between Prognostic Nutritional Index levels and worse 5-year outcomes.
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The present study was a prospective cohort study with a long follow-up time, which can improve the scientific nature of the results.
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This study is a single cohort design, which can be susceptible to selection bias.
Introduction
Due to the prevalence of unhealthy lifestyles, the large number of people with risk factors, and the rapid ageing of the population, the morbidity and mortality of cardiovascular diseases in China will continue to rise, and the turning point has not yet come.1 Therefore, identifying the residual risk of cardiovascular diseases and early risk stratification are necessary to more effectively tailor risk reduction strategies.2–4 Moreover, an increasing number of researchers have focused on investigating predictive markers of long-term prognosis in patients with coronary artery disease (CAD). Biomarkers of inflammation and immunity, which are associated with the initiation, progression and instability of atherosclerotic plaques,5 seem to be associated with future cardiovascular events, so additional studies investigating specific correlations are needed.
Nutritional status is a priority in risk stratification because it can be modified by health status and lifestyle interventions.6 7 The Prognostic Nutritional Index (PNI) is a simple and inexpensive index reflecting inflammation-based nutritional status. Buzby et al8 first proposed assessing the prognosis of patients who underwent gastrointestinal surgery in 1980. In 1984, Onodera used immune-nutritional indicators to calculate the PNI according to the following equation9: 10× serum albumin (ALB; g/dL) + 0.005 × total lymphocyte count (TLC; per mm3). As the most well-accepted nutritional index, a decreased PNI has been shown to predict the prognosis of cancer,10–12 pulmonary embolism13 and other diseases. The optimal cut-off value varies among studies. To our knowledge, there are currently few data from additional larger cohorts reporting the prognostic value of extremely high PNI values in any disease. Therefore, the aim of this prospective study was to evaluate the association between the baseline PNI and adverse clinical outcomes in patients with CAD.
Methods
Study design
The Personalized Antiplatelet Therapy According to CYP2C19 Genotype in Coronary Artery Disease (PRACTICE) study was conducted at Xinjiang Medical University Affiliated First Hospital from December 2016 to October 2021, which was a large, single-centre, prospective cohort study (identifier: NCT05174143).14 Written informed consent was obtained from all patients before the intervention. This study was a secondary analysis of the PRACTICE study. We analysed the data of 15 250 participants with CAD enrolled in the PRACTICE study. CAD was diagnosed as having at least one significant coronary artery stenosis of ≥70% luminal diameter, as shown by coronary angiography. A total of 267 patients were excluded due to unavailable ALB and TLC data, acute infection, malignancy, or hepatic and renal dysfunction. Finally, 14 983 patients with CAD were prospectively enrolled in this study. The PNI was calculated according to the following formula: 10× serum albumin (g/dL) + 0.005 ×TLC (per mm3). Additionally, the 50th and 90th percentiles of the PNI in the total cohort were calculated. By using two cut-off limits, we divided all participants into three groups: Q1 (PNI <51.35 (lower 50th percentiles), n=7515); Q2 (51.35 ≤PNI < 59.80 (50th to 90th percentiles), n=5958); and Q3 (PNI≥59.80 (upper 90th percentiles), n=1510). Figure 1 shows the flow chart of the study.
Flow chart of the Personalized Antiplatelet Therapy According to CYP2C19 Genotype in Coronary Artery Disease (PRACTICE) study. CAD, coronary artery disease; PNI, Prognostic Nutritional Index. Alb, albumin; TLC, total lymphocytes count.
Data collection
The clinical and demographic data of patients, including sex, age, smoking status, drinking status, acute coronary syndrome (ACS), history of hypertension and diabetes and heart rate, were collected after admission. Participants had their fasting blood drawn within 24 hours after admission, and the blood samples were stored in −80°C refrigerators until test. Serum concentrations of estimated glomerular filtration rate (eGFR), uric acid (UA), total cholesterol (TC), high-density lipoprotein-C (HDL-C), low-density lipoprotein-C (LDL-C), TLC and albumin (Alb) were measured in the Clinical Laboratory Department of the First Affiliated Hospital of Xinjiang Medical University using chemical analysis equipment. Medications were also collected by medical records review and self-reports.
We defined hypertension as a systolic blood pressure ≥140 mm Hg and/or diastolic blood pressure ≥90 mm Hg more than twice on different days or antihypertensive therapy.15 Diabetes mellitus was diagnosed by the use of hypoglycaemic agents, an HbA1c >6.5%, a fasting blood glucose ≥7.1 mmol/L, or a 2-hour blood glucose of oral glucose tolerance test ≥11.1 mmol/L, as well as a clear diabetes history.16
Assessment of the outcome and follow-up
The primary outcome measure of the study was mortality, including all-cause mortality (ACM) and cardiac mortality (CM), which was reported as death due to CAD, cardiogenic shock or sudden death. Secondary outcome measures were to assess major adverse cardiac events (MACE), covering ACM, CM, non-fatal myocardial infarction and unplanned coronary revascularisation, and major adverse cardiac and cerebrovascular events (MACCE), which were defined as MACE plus stroke.17 Enrolled patients were routinely followed up through visits, phone calls and questionnaire surveys at five time points after discharge (1 month, 6 month, 1 year, 3 years and 5 years). The research coordinators were well trained and blinded to the objectives of the current study.
Statistical analyses
In this study, SPSS V.26.0 for Windows statistical software (SPSS) and R (V.4.0.3) were used to perform the statistical analysis. The PNI is reported as continuous data, and all patients were classified into three groups according to the 50th and 90th percentiles of the PNI. Means with SD were used to report continuous variables, and frequencies with percentages were used to report nominal variables. We compared continuous and categorical variables using t tests, analysis of variance, χ2 tests or Fisher’s exact tests, as appropriate. A Kaplan-Meier analysis was used to express the cumulative incidence rate of long-term events. The continuous relationship between the PNI and the risk of long-term outcomes was illustrated by a restricted cubic spline. After adjusting for smoking history, drinking status, diabetes status, age, sex, HDL-C and LDL-C, Cox proportional hazards models were used to calculate HRs and 95% CIs for adverse outcomes. A value of p <0.05 was considered statistically significant.
Patients and public involvement
None.
Results
Baseline characteristics
Most of the 14 983 participants with CAD were men (73.9%), and the mean age was 60.16±11.54 years. There were 10 263 (68.6%) patients with hypertension and 7067 (47.2%) patients with diabetes. Albumin was 41.08±6.31 g/L, TLC was (2.18±0.80) ×109/ L, and PNI was 51.99±7.68. Several variables among the three groups, such as sex, age, smoking status, alcohol use, heart rate, diabetes history, eGFR, UA, TC, HDL-C, LDL-C, TLC, Alb and ACS had significant differences (all p <0.05). Only hypertension history and antiplatelet therapy drugs were not significantly different among the three groups (p ≥0.05). More details on the baseline characteristics of the study cohort are displayed in table 1.
Baseline characteristics of participants
Clinical outcomes
A total of 448 ACM, 333 CM, 1162 MACE and 1276 MACCE were recorded, and there were significant differences in the incidence of adverse outcomes among the three groups (p<0.001, see table 2). The incidence of ACM in Q1 group, Q2 group and Q3 group was 338 (4.5%), 77 (1.3%) and 33 (2.2%), respectively. Online supplemental figure displayed the Kaplan-Meier curves for ACM, CM, MACE and MACCE. We also plotted the restricted cubic splines to estimate the relative HR in all populations, and we found a J-shaped relationship between the PNI and worse 5-year outcomes, including ACM, CM, MACE and MACCE (see figure 2).
Supplemental material
Outcomes comparison of three groups
Furthermore, we assessed the prognostic value of different PNI levels in enrolled patients by Cox regression analysis. Compared to those in the Q2 group, only patients with extremely high PNI values in the Q3 group had a greater risk of ACM (Q3 vs Q2, HR: 1.617, 95% CI 1.012 to 2.585, p= 0.045), while those with low PNI values in the Q1 group had greater risk of ACM (Q1 vs Q2, HR = 1.995, 95% CI 1.532 to 2.598, p< 0.001) (table 3) and CM (Q1 vs Q2, HR = 2.113, 95% CI 1.551 to 2.880, p< 0.001) (online supplemental table 1). The risk of MACE (Q1 vs Q2, HR = 1.139, 95% CI 0.995 to 1.303, p = 0.058; Q3 vs Q2, HR = 1.142, 95% CI 0.907 to 1.437, p = 0.258, respectively) (online supplemental table 2) or MACCE (Q1 vs Q2, HR = 1.087, 95% CI 0.957 to 1.235, p = 0.198; Q3 vs. Q2, HR = 1.125, 95% CI 0.905 to 1.398, p = 0.290; respectively) (online supplemental table 3) in the Q1 and Q3 groups did not significantly differ, using Q2 as a reference.
Association between Prognostic Nutritional Index levels and all-cause mortality
Restricted cubic spline plots for mortality according to Prognostic Nutritional Index (PNI) on a continuous scale. Solid red lines are HRs, with dotted ribbons showing 95% CIs. ACM, all-cause mortality; CM, cardiac mortality; MACE, major adverse cardiovascular events; MACCE, major adverse cardiac and cerebrovascular events.
Discussion
In this prospective study, we evaluated the relationship between the PNI and adverse outcomes in patients with CAD. There were differences in sex, age, smoking status, drinking status, heart rate, diabetes history, eGFR, UA, TC, HDL-C, LDL-C, TLC and Alb among the three groups. Notably, patients with a low PNI (lower than the 50th percentile) had the lowest TLC and albumin levels. Those with extremely high PNI values (upper 90th percentiles) tend to have poor baseline conditions, such as higher UA levels, higher TC levels and a greater incidence of diabetes. Patients in the Q1 and Q3 groups had significantly greater incidences of ACM, CM, MACE and MACCE than those in the Q2 group. Restricted cubic splines revealed that there was a J-shaped relationship between the PNI and adverse outcomes in patients with CAD. After adjustment for potential confounding risk factors, we found that only patients with extremely high PNI values in the Q3 group had a greater risk of ACM, while those with low PNI values in the Q1 group had a greater risk of ACM and CM. To the best of our knowledge, the present study was the first to identify that patients with CAD with extremely high PNI values tend to have significantly greater incidences of ACM.
Findings from previous small-scale studies have indicated that a decreased PNI is associated with poor prognosis in patients with heart failure,18 coronary collateral development,19 arrhythmic events,20 in-stent restenosis,21 dilated cardiomyopathy,22 ACS6 and stable CAD.23 Consistent with these studies, our study emphasised the adverse prognostic significance of low PNI values for the overall population with CAD. The low PNI group had the lowest ALB concentration and TLCs among the three groups. The mechanism by which a decrease in the ALB concentration increases cardiovascular risk is mainly due to a decrease in the antioxidative ability, oncotic pressure-maintaining and antithrombotic ability of ALB.24 Inflammation plays an important role in the progression of coronary atherosclerosis.25 A reduced TLC indicates that the immune system is damaged by malnutrition, reflecting increased susceptibility to infection and inflammation of body,26 This accelerates the progression of atherosclerosis and leads to a poor prognosis,5 which might explain why individuals with a low PNI have a greater risk of adverse outcomes in patients with CAD.
We were surprised to find a J-shaped curve for poor long-term prognosis with increasing PNI . Yılmaz et al reported that a higher PNI can predict the LV dysfunction in patients who are newly diagnosed with hypertension.27 Similarly, extremely high PNI levels in patients with CAD were associated with ACM in our study. However, the pathogenetic mechanisms underlying this association are still unknown. Scholars have clearly established that nutritional status has a greater impact on prognosis than obesity in patients with cardiovascular disease.28–30 A previous study reported that there was no influence of COVID-19 diagnosis on mortality after percutaneous coronary intervention (PCI).31 Therefore, we did not consider the effects of the epidemic and obesity in this analysis. Some studies have suggested detrimental outcomes in PCIs performed during off-hours.32 Therefore, PCI during off-hours may have influenced our results. Notably, the serum ALB concentration and TLC in the high PNI group were the highest among the three groups. We believe that an elevated PNI may be caused by increased TLC secondary to a proinflammatory response, which is the major underlying mechanism of adverse outcomes in higher PNI groups. Additionally, as a negative reactant of the acute phase, albumin is more susceptible to acute inflammation because it can be inhibited by proinflammatory cytokines under systemic inflammatory conditions.33 Previous studies reported that different lymphocyte subsets play opposite roles: T helper-1 and B2 cells accelerate the occurrence of atherosclerosis, while regulatory T cells and B1 cells had the function of resisting atherosclerosis.34 The proportions of lymphocyte subsets can be regulated by nutritional status, leading to an imbalance between the proatherogenic and antiatherogenic immune microenvironments.26 This may be a potential mechanism by which a high PNI induces ACM through lymphocyte activity.
Study limitations
This study included a large-scale prospective cohort, which improved the statistical power of the results. The follow-up duration, up to 5 years, was the longest compared with that in previous similar studies. However, some limitations should be mentioned. First, we did not collect data on other indicators of inflammation or chronic inflammatory diseases. We collected only the baseline albumin and TLC data during the study duration and did not analyse the effect of dynamic changes in the PNI. Second, the mechanism of action between ALB and lymphocytes requires further study. Third, this was a single-centre cohort study. Therefore, our results must be further verified by multicentre studies.
Conclusions
The present study demonstrated that the baseline PNI may be a powerful, inexpensive and easily calculated predictor of adverse outcomes in patients with CAD, with a greater risk of ACM at extremely high PNI values. Our findings provide new insight into the early risk stratification of patients with CAD in clinical practice.
Data availability statement
Data are available upon reasonable request.
Ethics statements
Patient consent for publication
Ethics approval
The study was approved by the ethics committee of Xinjiang Medical University Affiliated First Hospital. Informed consent was obtained from all patients before the intervention.
Acknowledgments
The authors acknowledged all nurses and doctors in the Heart Centre of Xinjiang Medical University Affiliated First Hospital. In addition, we acknowledged all patients of blood samples.
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