The leuko-glycemic index can predict ischemia in myocardial perfusion scintigraphy

Introduction

Cardiovascular diseases are the primary global cause of mortality. Myocardial ischemia (MI) is a severe condition with numerous complications high death rates, and a poor prognosis (1). Every day, numerous individuals with chest pain receive evaluations in outpatient clinics. In order to diagnose MI, appropriate individuals are oriented to undergo myocardial perfusion scintigraphy (MPS), stress electrocardiography, or stress echocardiography (2).

Inflammation plays a substantial part in initiating the progression of atherosclerosis. Research has established that multiple types of cells and substances that cause inflammation play a role in the development of MI (3). The activation and rapid increase in inflammation might cause inflammatory markers of MI to indicate the body’s pathophysiological condition (4).

High blood sugar levels can contribute to the development of blood clots and the breakdown of blood clots, which can ultimately result in the formation of plaques in the arteries (5). Leukocytes play a crucial role in inflammatory diseases. Increased leukocytes have been found to have a strong correlation with the development of atherosclerosis and cardiovascular disease (6). Thus, leukocyte levels have proven to be a crucial factor in evaluating the risk of cardiovascular disease. The leuko-glycemic index (LGI) is a straightforward and practical index that combines leukocyte count and blood glucose value, making it a valuable tool for assessing MI (7). Past research has indicated that LGI is a reliable indicator of acute myocardial infarction and stroke (8).

In our study, we aimed to assess the diagnostic efficacy of LGI in determining MI by contrasting it with MPS. We present this article in accordance with the STROBE reporting checklist (available at https://jxym.amegroups.com/article/view/10.21037/jxym-24-32/rc).

Methods

Study design and subject

This retrospective research was performed between January 2021 and December 2023. Patients referred for MPS from cardiology outpatients clinic with chest pain were enrolled. Exclusion criteria were prior coronary artery disease (CAD), hyperthyroidism, hypothyroidism, valve disease, heart failure, renal failure, systemic inflammatory disease, active infection or cancer, using steroids or anticoagulants, hepatic disease, morbidly obese, pulmonary disease, congenital heart disease, hematological disease, unstable angina pectoris and myocardial infarction after troponin and electrocardiogram (ECG) evaluation. This research eliminated participants for whom data were unavailable and whose written patient informed consent form was not signed. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the regional ethics board of Gazi Yasargil Training and Research Hospital (No. 405) and informed consent was obtained from all individual participants.

Study protocol

Socio demographic and clinical data of the patients were obtained from archive files. Blood cell counts of all patients were obtained from their records. According to MPS, patients were split into two as ischemic and non-ischemic. Ischemic MPS results were then confirmed by coronary angiography. Routine blood test studied before performing MPS.

Cardiac perfusion imaging

We conducted a stress/rest imaging protocol over a span of two days utilizing Technetium 99-m methoxy-isobutylisonitrile (Tc-99m MIBI) to assess myocardial perfusion. During the highest level of physical activity, we administered radiopharmaceutical agents either using the modified Bruce procedure or during the peak of hyperemia. We employed dipyridamole (0.142 mg/kg/min) or adenosine (0.28 mg/min) infusion as a radiopharmaceutical agents. The imaging procedure commenced 30–45 minutes following the administration of 15–20 millicuries of Tc-99m MIBI.

Protocol for MPS imaging

We captured all images within a 180° angle orbit, ranging from a right anterior oblique position to a 45° angle to the left posterior oblique position. This was done using a dual-head γ-camera (GE Healthcare, Wauwatosa, WI, USA) that was equipped with a resolution collimator, a 64×64 matrix. The acquisition process involved taking images at 3° intervals over the 180° angle, with a total of 60 projections and a duration of 9–13 seconds per projection. The camera was set to use a 20% energy window centered on the 140 keV photopeak of Tc-99m. The image sets acquired during single photon emission tomography (SPECT) analysis were reconstructed using the white-beam reconstruction (WBR) and evolution for cardiac suggested parameters on a dedicated workstation (GE Healthcare, Haifa, Israel). The reconstruction was done with computed tomography (CT) based attenuation correction. Following each capture, a low-dose CT scan of the chest was conducted using specific parameters (1.0 mA; 0.2–0.3 ms; 100 keV). The purpose of this scan was to generate attenuation maps to fix the emission data. The MPS dataset was meticulously aligned with the CT mapping to provide the scans that have been adjusted for reduction.

Definitions

White blood cell count was measured in cells per millimeter, while blood glucose levels were reported in mg/dL. Both values were multiplied and divided by 1,000 to determine the LGI.

Laboratory analysis

The whole blood count was calculated using an automated hematology analyzer manufactured by Sysmex Corporation (Kobe, Japan). Total leukocyte count and differentiation, hemoglobin, hematocrit, platelet levels, were documented as blood parameters. Additionally, glucose, creatine, albumin and lipit panel values were measured utilizing a Mindray Chemistry Analyzer instrument (BS-2000M, Shenzhen, China).

Statistical analysis

The study was conducted using IBM SPSS software, specifically version 24.0. The mean, standard deviation, or median are used to represent initial continuous variables, specifically the interquartile range. The normality of the variable distribution was assessed using the Kolmogorov-Smirnov and Shapiro-Wilk tests. Categorical variables were represented using frequencies and percentages. The Chi-squared or Fisher’s exact test was used to analyze categorical variables. The statistical tests employed to assess continuous variables were the Student’s t-test or the Mann-Whitney U-test. The threshold for significance in all tests was fixed at 0.05.

Results

A total of 143 patients, comprising 44 females (30.8%) and, 99 males (69.2%) with a median age of 60 (43–77) years, participated in the research. Individuals were classified into two groups: non-ischemic (n=87) and ischemic (n=56). The patient’s clinical characteristics and laboratory results were expressed in Tables 1,2, respectively. There is no statistically significant among the groups regarding dyslipidemia (P=0.11), smoking (P=0.17), and gender (P=0.20). There were no substantial varying across the groups including laboratory parameters (P>0.05). LGI values were substantially higher in the ischemic group (P<0.001). In the univariate and multivariate analysis, LGI was predictor for ischemia in MPS [1.06; 95% confidence interval (CI): 1.01 to 1.12; P=0.01 and 1.08; 95% CI: 1.05 to 1.15; P=0.02] (Table 3). The area under the curve (AUC) of the LGI to predict ischemic patients in MPS was 0.752 (95% CI: 0.67 to 0.88; P<0.001) (Figure 1). In addition, the optimal cut-off value of LGI was 1,873 mg/dL·mm³ (sensitivity =72%; specificity =70%).

Figure 1 The AUC of the LGI to predict ischemic patients. AUC, area under the curve; CI, confidence interval; LGI, leuko-glycemic index.

Discussion

In this study, we examined the correlation between LGI and MPS detected ischemia in patients with chest pain. LGI showed a significant diagnostic capability for distinguishing MI detected by MPS.

The LGI includes two readily measurable indicators: leukocyte count and blood glucose level. Leukocytes serve as the primary agents of inflammation. An elevated leukocyte count indicates that the body is experiencing inflammation. The leukocyte count in the blood of individuals with MI is strongly associated with the occurrence of heart failure, cardiogenic shock, and mortality (9,10). Research has demonstrated that the leukocyte count serves as an indicator of death risk in individuals with MI, and a greater leukocyte count is linked to higher rates of mortality during hospitalization or in the immediate aftermath of MI (11). Stress-induced secretion from inflammatory mediators influences glucose metabolism and contributes to the development of hyperglycemia (12). Patients with MI frequently encounter high blood sugar levels, irrespective of their medical history of diabetes (13).

The first description of LGI was provided by Quiroga et al. for predicting MI (14). León-Aliz et al. stated that a high LGI value can be indicative of increased risk of mortality during hospitalization (15). A study conducted by Rodriguez-Jiménez et al. revealed a correlation between elevated LGI value and the presence of stable angina pectoris (16). Studies conducted by Caldas et al. and García Alvarez et al. have demonstrated that the LGI may be used as a reliable indicator for predicting mortality in cases of ischemic stroke (17,18).

The current guidelines advocate using coronary computer tomography (CCT) to diagnose CAD in patients who are suspected to have CAD (19). Despite the acceptable levels of reliability and efficiency exhibited by the new methods, MPS continues to be the preferable non-invasive test due to its straightforward implementation and economical cost (20). CCT also fails to provide a hemodynamic response in the absence of a fractional flow reserve. Despite minor variations across different methodologies and application domains, MPS maintains a 90% level of sensitivity and specificity (21,22). Spectral perfusion scanning has been reported with higher levels of inflammatory markers in individuals with MI but has not been compared with LGI.

In their study, Sadeghi et al. found that the value of LGI had an AUC of 0.77 in predicting mortality (23). We found an AUC of 0.75 for MI prediction. Moreover, it should be kept in mind that our patient population was not Hispanic. Studies have used different thresholds for LGI. The reported cut offs vary significantly, ranging from 656 to 2,200 mg/dL·mm³ (24). Qi et al. reported an ideal cut-off 1,402 mg/dL·mm³ (25). The optimal cut-off value of LGI for predicting ischemic patients in the MPS was determined to be 1,873 mg/dL·mm³ in our study. Several studies have included the LGI into the traditional risk score. Hirschson Prado et al. found that adding LGI into the thrombolysis in myocardial infarction (TIMI) risk score improved their ability to differentiate ST-elevation myocardial infarction (STEMI) patients (26).

Limitations

There were certain constraints in this investigation. Initially, it is important to note that this study was conducted retrospectively. The limited sample size may have resulted in data bias. Furthermore, the study did not include the collection of cardiac ultrasonography data. Moreover, the comparative analysis of LGI’s predictive value with other prognostic scores was not conducted. Also, there was a lack of information regarding the length, classification, and management of diabetes.

Conclusions

We found that LGI measurement may be used as a diagnostic method to estimate ischemia in patients with chest pain who underwent MPS due to MI. However, we believe that larger studies are needed on this subject and if these studies support our findings, LGI measurement will play an important role in clinical evaluation.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jxym.amegroups.com/article/view/10.21037/jxym-24-32/rc

Data Sharing Statement: Available at https://jxym.amegroups.com/article/view/10.21037/jxym-24-32/dss

Peer Review File: Available at https://jxym.amegroups.com/article/view/10.21037/jxym-24-32/prf

Funding: None.

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://jxym.amegroups.com/article/view/10.21037/jxym-24-32/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the regional ethics board of Gazi Yasargil Training and Research Hospital (No. 405) and informed consent was obtained from all individual participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Chaitman BR, Alexander KP, Cyr DD, et al. Myocardial Infarction in the ISCHEMIA Trial: Impact of Different Definitions on Incidence, Prognosis, and Treatment Comparisons. Circulation 2021;143:790-804. [Crossref] [PubMed]
2. Gimelli A, Pugliese NR, Buechel RR, et al. Myocardial perfusion scintigraphy for risk stratification of patients with coronary artery disease: the AMICO registry. Eur Heart J Cardiovasc Imaging 2022;23:372-80. [Crossref] [PubMed]
3. Kologrivova I, Shtatolkina M, Suslova T, et al. Cells of the Immune System in Cardiac Remodeling: Main Players in Resolution of Inflammation and Repair After Myocardial Infarction. Front Immunol 2021;12:664457. [Crossref] [PubMed]
4. Donia T, Khamis A. Management of oxidative stress and inflammation in cardiovascular diseases: mechanisms and challenges. Environ Sci Pollut Res Int 2021;28:34121-53. [Crossref] [PubMed]
5. Ambreen S, Fatima S, Elwakiel A, et al. Hypercoagulability Impairs Plaque Stability in Diabetes-Induced Atherosclerosis. Nutrients 2022;14:1991. [Crossref] [PubMed]
6. Henein MY, Vancheri S, Longo G, et al. The Role of Inflammation in Cardiovascular Disease. Int J Mol Sci 2022;23:12906. [Crossref] [PubMed]
7. Kilic O, Buber I, Kahraman F. Predicting the Severity of Coronary Artery Disease: Can the Leukocyte Glucose Index be Used? J Coll Physicians Surg Pak 2022;32:1519-23. [Crossref] [PubMed]
8. Seoane LA, Burgos L, Espinoza JC, et al. Prognostic Value of the Leuko-Glycaemic Index in the Postoperative Period of Coronary Artery Bypass Grafting. Braz J Cardiovasc Surg 2021;36:484-91. [Crossref] [PubMed]
9. Samsky MD, Morrow DA, Proudfoot AG, et al. Cardiogenic Shock After Acute Myocardial Infarction: A Review. JAMA 2021;326:1840-50. Erratum in: JAMA 2021;326:2333. [Crossref] [PubMed]
10. Sasmita BR, Zhu Y, Gan H, et al. Leukocyte and its Subtypes as Predictors of Short-Term Outcome in Cardiogenic Shock Complicating Acute Myocardial Infarction: A Cohort Study. Shock 2022;57:351-9. [Crossref] [PubMed]
11. Smith RJ, Sarma D, Padkins MR, et al. Admission Total Leukocyte Count as a Predictor of Mortality in Cardiac Intensive Care Unit Patients. JACC Adv 2023;3:100757. [Crossref] [PubMed]
12. Vedantam D, Poman DS, Motwani L, et al. Stress-Induced Hyperglycemia: Consequences and Management. Cureus 2022;14:e26714. [PubMed]
13. Alkatiri AH, Qalby N, Mappangara I, et al. Stress hyperglycemia and poor outcomes in patients with ST-elevation myocardial infarction: a systematic review and meta-analysis. Front Cardiovasc Med 2024;11:1303685. [Crossref] [PubMed]
14. Quiroga W, Conci E, Zelaya F, et al. Estratificación del riesgo en el infarto agudo de miocardio según el índice leucoglucémico.¿ El Killip-Kimball de laboratorio. Rev Fed Arg Cardiol 2010;39:29-34.
15. León-Aliz E, Moreno-Martínez FL, Pérez-Fernández GA, et al. Leuko-glycemic index as an in-hospital prognostic marker in patients with ST-segment elevation myocardial infarction. Clin Investig Arterioscler 2014;26:168-75. [PubMed]
16. Rodriguez-Jiménez A, Fardales-Rodríguez R, Toledo-Rodríguez E, et al. Leuko-glycemic Index as a Prognostic Factor after Acute Myocardial Infarction with ST-segment Elevation. Revista Finlay 2019;9:97-107.
17. Caldas FA, Iaconis Campbell JD. Valor pronóstico del índice leucoglucémico en el stroke isquémico agudo. Buenos Aires: Asociación Médica Argentina; 2012 [cited 2021 Apr 19]. Available online: https://repositorio.barcelo.edu.ar/greenstone/collect/investig/index/assoc/HASH0183.dir/AMA%20Caldas%20Federico%252C%20Iaconis%20Campbell%20Juan.pdf
18. García Alvarez PJ, García Albero ÁP, Santana Álvarez CJ. Leukoglycaemic index as a predictor in short term of mortality in stroke. Rev Arc Med Camaguey 2018;22:163-70.
19. Sciagrà R, Lubberink M, Hyafil F, et al. EANM procedural guidelines for PET/CT quantitative myocardial perfusion imaging. Eur J Nucl Med Mol Imaging 2021;48:1040-69. [Crossref] [PubMed]
20. Villemain O, Baranger J, Jalal Z, et al. Non-invasive imaging techniques to assess myocardial perfusion. Expert Rev Med Devices 2020;17:1133-44. Erratum in: Expert Rev Med Devices 2020;17:1239. [Crossref] [PubMed]
21. Chan HP, Chang CC, Hu C, et al. The Evaluation of Left Ventricle Ischemic Extent in Patients with Significantly Suspicious Cardiovascular Disease by 99mTc-Sestamibi Dynamic SPECT/CT and Myocardial Perfusion Imaging: A Head-to-Head Comparison. Diagnostics (Basel) 2021;11:1101. [Crossref] [PubMed]
22. Romero-Farina G, Aguadé-Bruix S. Equilibrium radionuclide angiography: Present and future. J Nucl Cardiol 2021;28:1315-22. [Crossref] [PubMed]
23. Sadeghi R, Roshdi Dizaji S, Vazirizadeh-Mahabadi M, et al. Prognostic Value of The Leuko-Glycemic Index in Acute Myocardial Infarction; a Systematic Review and Meta-Analysis. Arch Acad Emerg Med 2023;11:e25. [PubMed]
24. Cuesta-Mero PL, García-Romo LA, Villacres-López ÁD. Leukoglucemic index as a prognostic marker for complications in patients with acute myocardial infarction. Rev Colomb Cardiol 2021;28:119-27.
25. Qi LY, Liu HX, Cheng LC, et al. Prognostic Value of the Leuko-Glycemic Index in Acute Myocardial Infarction Patients with or without Diabetes. Diabetes Metab Syndr Obes 2022;15:1725-36. [Crossref] [PubMed]
26. Hirschson Prado A, Higa C, Merlo P, et al. Prognostic value of the leuko-glycemic index in acute myocardial infarction. Results from the SCAR multicenter registry. Rev Argent Cardiol 2014;82:500-5. [Crossref]