Uric Acid/Albumin Ratio as A Novel Biomarker for Predicting Poor Coronary Collateral Circulation in Coronary Artery Disease: Meta-analysis Study

Б  том ХXXI, 2025, № 2 ДРУЖЕСТВО НА КАРДИОЛОЗИТЕ В БЪЛГАРИЯ АВТОРСКИ СТАТИИ ORIGINAL ARTICLES URIC ACID/ALBUMIN RATIO AS A NOVEL BIOMARKER FOR PREDICTING POOR URIC ACID/ALBUMIN RATIO AS A NOVEL BIOMARKER FOR PREDICTING POOR CORONARY COLLATERAL CIRCULATION IN CORONARY ARTERY DISEASE: CORONARY COLLATERAL CIRCULATION IN CORONARY ARTERY DISEASE: META-ANALYSIS STUDY META-ANALYSIS STUDY F. Farabi1, M. N.Afi fa2, P. Puspawikan1, B. B. Tiksnadi1 1Department of Cardiology and Vascular Medicine, Faculty of Medicine, University Padjadjaran 2Faculty of Medicine, University Indonesia – Jakarta, Indonesia СЪОТНОШЕНИЕТО МЕЖДУ ПИКОЧНА КИСЕЛИНА И АЛБУМИН КАТО СЪОТНОШЕНИЕТО МЕЖДУ ПИКОЧНА КИСЕЛИНА И АЛБУМИН КАТО НОВ БИОМАРКЕР ЗА ПРОГНОЗИРАНЕ НА ЛОША КОРОНАРНА КОЛАТЕРАЛНА НОВ БИОМАРКЕР ЗА ПРОГНОЗИРАНЕ НА ЛОША КОРОНАРНА КОЛАТЕРАЛНА ЦИРКУЛАЦИЯ ПРИ ИСХЕМИЧНА БОЛЕСТ НА СЪРЦЕТО: МЕТААНАЛИЗ ЦИРКУЛАЦИЯ ПРИ ИСХЕМИЧНА БОЛЕСТ НА СЪРЦЕТО: МЕТААНАЛИЗ Ф. Фараби1, М. Н. Афифа2, П. Пуспавикан1, Б. Б. Тикснади1 1Катедра по кардиология и съдова медицина, Медицински факултет, Университет Паджаджаран – Бандунг, Индонезия 2Медицински факултет, Университет Индонезия – Джакарта, Индонезия Abstract. Background: Coronary collateral plays a role in maintaining myocardial function, limiting infarct size, and reduced morbidity and mortality. Uric acid to albumin ratio (UAR) has recently been discovered as a novel biomarker associated with cardiovascular disease. However, studies investigating the relationship between UAR and coronary collateral circulation (CCC) formation are limited and have low magnitude. Therefore, this study aims to determine the relationship between UAR and CCC formation in coronary artery disease patients. Methods: The literature was searched in PubMed, Medline, SpringerLink, ScienceDirect, and Scopus for articles published before August 2024. Studies that did not report UAR results as one of the parameters but included uric acid and albumin in their laboratory parameters, had UAR values calculated using the error propagation for ratio formula. Statistical analysis for meta-analysis using Review Manager 5.4 software to evaluate the relationship between UAR and CCC. Results: A systematic search retrieved nine studies that met the eligibility criteria. The total population in these studies was 5290 subjects, including 3744 subjects with good coronary collateral circulation and 1546 subjects with poor coronary circulation. Four of the nine studies assessed albumin parameters using glycated albumin and one study assessed albumin parameters using ischemic modifi ed albumin. The uric acid/albumin ratio is correlated with CCC formation, with the lower the UAR, the more likely CCC formation will occur (SMD -0.31, 95% CI -0.56 to -0.05, p-value 0.02, I2 93%). In the subanalysis of the different parameters, uric acid is also correlated with CCC formation (SMD -0.41, 95% CI -0.74 to -0.08, p-value 0.01, I2 96%), while albumin has no strong correlation. Conclusion: Uric acid to albumin ratio could be a new parameter in predicting the formation of coronary collateral circulation in patients with stable coronary artery disease. Key word: albumin, CAD, coronary collateral circulation, uric acid Address for correspondence: Fatih Farabi, MD, Department of Cardiology and Vascular Medicine, University Padjadjaran, Bandung, Indonesia, e-mail: [email protected] Резюме.Въведение: Колатералите на коронарните съдове играят роля за поддържането на миокардната функция, ограничаването на размера на инфаркта и намалената заболяемост и смъртност. Съотношението пикочна киселина/ албумин (UAR) наскоро е открито като нов биомаркер, свързан със сърдечно-съдовите заболявания. Въпреки това проучванията, изследващи връзката между UAR и формирането на коронарна колатерална циркулация, са ограничени и с ниска значимост. Това проучване цели да се определи връзката между UAR и формирането на коронарна колатерална циркулация при пациенти с коронарна артериална болест. Методи: Проведено е литературно търсене в PubMed, Medline, SpringerLink, ScienceDirect и Scopus на статии, публикувани преди август 2024 г. За проучванията, които не докладват резултати за UAR като един от параметрите, но включват пикочна киселина и албумин в лабораторните си параметри, стойностите на UAR са изчислени с помощта на формула за разпространение This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. doi: 10.3897/bgcardio.31.e142199 F. Farabi, M. N.Afi fa, P. Puspawikan, B. B. Tiksnadi 100 B Coronary artery disease (CAD) is caused by narrowing of the coronary arteries, which leads to ischemia. The reduced oxygen supply causes the heart muscle to work at suboptimal levels [1, 2]. In ischemic conditions, coronary blood vessels generate additional blood fl ow to reduce ischemia, called collateral circulation. Coronary collateral arteries originate from other coronary arteries with better blood fl ow to help perfuse the ischemic region of the heart [3]. The process of coronary collateral formation begins with the mechanism of fl uid shear stress at the endothelial level and the presence of ischemia-induced infl ammatory factors. Coronary collateral plays a role in maintaining myocardial function, limiting infarct size, and reduced morbidity and mortality [4, 5]. Infl ammation is a contributing factor to collateral formation. Uric acid is a parameter that has been found to be associated with infl ammatory conditions. Serum uric acid is the end product of purine metabolism. Uric acid plays a role in the infl ammatory process and may impair vascular endothelial function [6, 7]. Uric acid has been associated with cardiovascular disease and its prognosis. Hypertension, heart failure, CAD, and arrhythmias are some of the cardiovascular diseases associated with elevated uric acid [8]. Meanwhile, albumin is one of the parameters in the blood that has anti-infl ammatory and protective eff ects against oxidative stress [9, 10] Hypoalbuminemia is associated with increased mortality and incidence of cardiovascular disease [11]. Therefore, uric acid and albumin have opposite eff ects on cardiovascular disease. Uric acid to albumin ratio (UAR) has recently been discovered as a novel biomarker associated with cardiovascular disease. Higher UAR levels are associated with increased severity in CAD patients [12, 13] However, studies investigating the relationship between UAR and CCC formation are limited and have low magnitude. Therefore, this study aims to determine the relationship between UAR and CCC formation in coronary artery disease patients. M Study Design and Literature Search This study was registered at the International Prospective Register of Systematic Reviews (PROSPERO) with the registration number CRD42024575003. The literature was searched in PubMed, Medline, SpringerLink, ScienceDirect, and Scopus for articles published before August 2024. The search used the keywords “acute myocardial infarction”, “uric acid”, “albumin”, and “coronary collateral circulation”. The detailed key words used are listed in the Table 1. Table 1. Search keywords in the database Database Key word PubMed, Medline, ScienceDirect, Scopus (“Coronary Artery Disease*” OR “CAD” OR “Atheroslero*” OR “Coronary Atherosclero*” OR “Chronic total occlu*”) AND (“Uric Acid” OR “Albumin” OR “Uric Acid to Albumin Ratio” OR “Uric Acid to Albumin” OR “Uric Acid/Albumin” OR “UAR”) AND (“Coronary Collateral Circulation*” OR “CCC” OR “Rentrop” OR “Coronary Collateralization” OR “Coronary Collateral*” OR “Collateral Index”) SpringerLink (“Uric Acid to Albumin Ratio” OR “Uric Acid to Albumin” OR “UAR”) AND (“Coronary Collateral Circulation” OR “CCC” OR “Rentrop” OR “Coronary Collateralization” OR “Collateral Index”) на грешките за формулата на съотношението. Статистическия анализ в този метаанализ е извършен чрез софтуер Review Manager 5.4, за да се оцени връзката между UAR и коронарната колатерална циркулация. Резултати: Чрез систематичното търсене се откриха 9 проучвания, отговарящи на критериите за допустимост. Общата популация в тези проучвания е 5290 лица, вкл. 3744 лица с добра коронарна колатерална циркулация и 1546 с лоша коронарна циркулация. В 4 от 9 проучвания албуминовите параметри се оценяват чрез гликирания албумин и в 1 проучване – чрез исхемично модифициран албумин. Съотношението пикочна киселина/албумин корелира с формирането на коронарна колатерална циркулация, като колкото по-ниско е UAR, толкова по-вероятно е образуването на коронарна колатерална циркулация (SMD -0,31, 95% CI -0,56 до -0,05, p-стойност 0,02, I2 93%). При поданализа на различните параметри пикочната киселина също корелира с формирането на коронарна колатерална циркулация (SMD -0,41, 95% CI -0,74 до -0,08, p-стойност 0,01, I2 96%), докато албуминът няма силна корелация. Заключение: Съотношението пикочна киселина/албумин може да е нов параметър за прогнозиране на формирането на коронарна колатерална циркулация при пациенти със стабилна коронарна артериална болест. Ключови думи:албумин, коронарна артериална болест (исхемична болест на сърцето), коронарна колатерална циркулация, пикочна киселина Адрес за кореспонденция: д-р Фатих Фараби, Катедра по кардиология и съдова медицина, Университет Паджаджаран, Бандунг, Индонезия, e-mail: [email protected] 101 Uric acid/albumin ratio as a novel biomarker for predicting... Eligibility Criteria Inclusion criteria for this study were studies with a population of patients with chronic stable coronary artery disease with or without chronic total occlusion who were measured for albumin and uric acid with the outcome of coronary collateral circulation. Glycated albumin and ischemia-modifi ed albumin measurements were also included in this study. Coronary collateral circulation was assessed using the Rentrop classifi cation, where grades 0-1 represent poor coronary collateral circulation and 2-3 represent good coronary collateral circulation. The study designs included in this study were cross-sectional, cohort, and case-control. Manuscripts that are not published in English and are not available as full papers will be excluded from this study. Data Extraction Eligible studies and those included in this study were screened by two independent reviewers, who initially screened the abstract and title. In case of disagreement between the two reviewers, the decision was taken by a third person with higher competence in the cardiovascular fi eld. Quality Assessment Risk of bias was assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Analytical Cross-Sectional Studies. This assessment was performed by two independent reviewers. The parameters assessed consisted of eight questions. The interpretation of the value is if it has a value of more than 70% is considered low risk category, 50% - 70% is considered moderate risk and less than 50% is considered high risk [14]. Outcome of Interest The primary outcome of this study is to fi nd the correlation between uric acid to albumin ratio to predict coronary collateral formation in patients with stable coronary artery disease. The secondary outcome of this study is the correlation between each uric acid and albumin on the development of coronary collateral circulation. Statistical Analysis Studies that did not report UAR results as one of the parameters but included uric acid and albumin in their laboratory parameters, had UAR values calculated. The UAR was calculated based on the mean ± standard deviation data of uric acid and albumin using the error propagation for ratio formula. When the studies do not provide mean and standard deviation data, it will be estimated using median and percentile (fi rst and third quartile) [15, 16]. Statistical analysis for meta-analysis using Review Manager 5.4 software to evaluate the relationship between UAR and CCC formation using the eff ect measure standard mean diff erence. Random eff ect model is used because the units of measurement for uric acid and albumin examination are not the same between each study. Heterogeneity is assessed using the I2 value, which indicates that if I2 > 50% and p value < 0.1, the heterogeneity category is high. Publication bias was measured using funnel plots and Egger’s test which was analyzed with IBM Corp. Released 2023. IBM SPSS Statistics for Windows, Version 29.0.2.0 Armonk, NY: IBM Corp. R A systematic search conducted in accordance with the research method yielded a total of 772 studies after the removal of duplicate studies. After reviewing the titles and abstracts, a total of 53 studies were obtained, which were then assessed by two reviewers to determine which studies would be included in this review. After reviewing the full papers, 9 studies were included in this review [17-25]. All studies included in this research were assessed for risk of bias using JBI, where eight of the nine studies had a low risk of bias and one study had a moderate risk of bias (Table 2). Fig. 1. Diagram of systematic search process based on PRISMA fl ow 2020 F. Farabi, M. N.Afi fa, P. Puspawikan, B. B. Tiksnadi 102 Table 2. Baseline characteristic of the study included in this review Study ID Study population Study Method Albumin parameter Good coronary collateral circulation Poor coronary collateral circulation Sample size Age Sex (Male) BMI Sample size Age Sex (Male) BMI Dong et al. 2024 1093 patients CAD and CTO Crosssectional Glycated albumin 775 59.1 ± 10.2 666 (85.9%) 26.23 ± 3.32 318 58.0 ± 10.1 270 (84.9%) 26.70 ± 3.21 Zhao et al. 2020 391 patients Stable CAD Crosssectional Albumin 298 60.83 ± 11.77 230 (77.2) - 93 63.06 ± 9.56 79 (84.9) - Shen et al. 2013 434 patients CAD and CTO Crosssectional Glycated albumin 95 (nondiabetes) and 199 (diabetes) 62.4 ± 10.4 (non-diabetes) and 64.6 ± 11.1 (diabetes) 83 (87.4%) in non-diabetes and 172 (84.6%) in diabetes 24.8 ± 3.0 (non-diabetes) and 25.1 ± 3.4 (diabetes) 22 (nondiabetes) and 118 (diabetes) 68 ± 11.1 (nondiabetes) and 66.5 ± 10.1 (diabetes) 10 (45.5%) in non-diabetes and 77 (65.3%) in diabetes 25.3 ± 2.8 (non-diabetes) and 25.8 ± 3.4 (non-diabetes) Toprak et al. 2023 415 patients CAD and CTO Crosssectional Albumin 232 57 ± 13 156 (67.2) 26.6 ± 4.0 183 57 ± 12 111 (60.7) 27.3 ± 4.3 Chen et al. 2020 128 patients CAD and CTO Crosssectional Ischemic modifi ed albumin 59 60.92 ± 11.61 45 (76.3) - 69 61.28 ± 11.61 45 (65.2) - Gao et al. 2022 792 patients CAD and CTO Crosssectional Glycated albumin 570 59.15 ± 10.40 493 (86.5%) 26.30 ± 3.33 222 58.51 ± 9.93 184 (82.9%) 26.62 ± 3.09 Liu et al. 2021 1653 CAD inpatients with CTO Cohort Glycated albumin 1298 59.1 ± 10.3 1074 (82.7) 26.0 ± 3.1 355 57.7 ± 10.5 294 (82.8) 28.1 ± 3.7 Akbuga et al. 2022 172 CAD patients with CTO Crosssectional Albumin 98 67.5 ± 10.2 80 (81.6) - 74 67.7 ± 10.7 53 (71.7) - Şaylık et al. 2023 212 CAD patients with CTO Crosssectional Albumin 120 59.5386 ± 14.2571 79 (65.8) 27.289 ± 3.3016 92 60.1761 ± 11.674 69 (75.0) 27.8 ± 2.862 103 Uric acid/albumin ratio as a novel biomarker for predicting... Baseline Characteristics The total population in these studies was 5290 subjects, including 3744 subjects with good coronary collateral circulation and 1546 subjects with poor coronary circulation. Four of the nine studies assessed albumin parameters using glycated albumin and one study assessed albumin parameters using ischemic modifi ed albumin (Table 3). Primary Outcome: Uric Acid to Albumin Ratio and Coronary Collateral Circulation Formation The uric acid/albumin ratio is correlated with CCC formation, with the lower the UAR, the more likely CCC formation will occur (SMD -0.31, 95% CI -0.56 to -0.05, p-value 0.02, I2 93%). Subanalysis of UAR in the two groups of albumin and glycated albumin examination showed similar results, showing that lower UAR was Table 3. Risk of bias assessed by the Joanna Briggs Institute (JBI) Critical Appraisal Tools for use in JBI Critical Appraisal Checklist for Analytical Cross-Sectional Studies Authors Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 % Yes Risk Shen et al. 2013  100% Low Zhao et al. 2020  ---  62.5% Moderate Chen et al. 2020  --  75% Low Liu et al. 2021  - -75% Low Akbuga et al. 2022  -- 75% Low Gao et al. 2022  -- 75% Low Toprak et al. 2023  --  75% Low Şaylık et al. 2023  --  75% Low Dong et. Al. 2024  100% Low Q1. Were the criteria for inclusion in the sample clearly defi ned? Q2. Were the study subjects and the setting described in detail? Q3. Was the exposure measured in a valid and reliable way? Q4. Were objective, standard criteria used for measurement of the condition? Q5. Were confounding factors identifi ed? Q6. Were strategies to deal with confounding factors stated? Q7. Were the outcomes measured in a valid and reliable way? Q8. Was appropriate statistical analysis used? : Yes; -: No. Fig. 2. Forest plot meta-analysis on the association between uric acid to albumin ratio and coronary collateral circulation formation Fig. 3. Subanalysis of UAR based on albumin and glycated albumin examination parameters F. Farabi, M. N.Afi fa, P. Puspawikan, B. B. Tiksnadi 104 more likely to result in CCC formation, but both groups were not statistically signifi cant. Secondary Outcome: Uric Acid and Albumin Levels to Coronary Collateral Circulation Formation In the analysis of the diff erent parameters, uric acid is also correlated with CCC formation (SMD -0.41, 95% CI -0.74 to -0.08, p-value 0.01, I2 96%), while albumin has no strong correlation. However, when subanalysis is performed based on albumin and glycated albumin parameters, both are correlated with CCC formation, but with opposite results. There were four studies for each parameter. Higher albumin increased the likelihood of CCC (SMD 0.22, 95% CI 0.10 to 0.34, p-value 0.0004, I2 0%), while lower glycated albumin increased the likelihood of CCC (SMD -0.24, 95% CI -0.35 to -0.14, p-value <0.001, I2 48%). Fig. 4. Forest plot meta-analysis on the association between uric acid and coronary collateral circulation formation Fig. 5. Forest plot meta-analysis on the association between albumin and coronary collateral circulation formation Fig. 6. Subanalysis based on albumin and glycated albumin examination parameters 105 Uric acid/albumin ratio as a novel biomarker for predicting... Heterogeneity and Publication Bias There was high heterogeneity in the primary and secondary outcome analysis, but the heterogeneity was not signifi cant in the subanalysis based on albumin measurement type. The albumin parameters infl uenced the heterogeneity score of the study. The funnel plot analysis showed a symmetric picture, and the results of the Egger’s test analysis showed a p-value of 0.69, indicating that there was no signifi cant publication bias in this study. Sensitivity Analysis Sensitivity analysis was performed by eliminating studies based on the type of albumin assayed and also by eliminating each study to see how each study aff ected the results. The results of the sensitivity analysis of the exclusion studies based on the type of albumin examined showed results that remained consistent but were not statistically signifi cant (Fig. 8, 9). This indicates that it is still less robust. However, the results of the sensitivity analysis by excluding each study showed consistent results (Table 4). Fig. 8. Sensitivity analysis by exclusion of glycated albumin parameters Fig. 9. Sensitivity analysis by exclusion of albumin parameters Table 4. Sensitivity analysis SMD (95% CI) I2 Omitting Shen et al. 2013 -0.30 (-0.58 - -0.01) 94% Omitting Zhao et al. 2020 -0.36 (-0.63 - -0.09) 94% Omitting Chen et al. 2020 -0.31 (-0.58 - -0.04) 94% Omitting Liu et al. 2021 -0.36 (-0.64 - -0.08) 93% Omitting Gao et al. 2021 -0.35 (-0.64 - -0.06) 94% Omitting Akbuga et al. 2022 -0.27 (-0.54 - -0.01) 94% Omitting Saylik et al. 2023 -0.16 (-0.33 - 0.01) 84% Omitting Toprak et al. 2023 -0.31 (-0.59 - -0.03) 94% Omitting Dong et al. 2024 -0.34 (-0.64 - -0.03) 94% Fig. 7. Funnel plot for publication bias F. Farabi, M. N.Afi fa, P. Puspawikan, B. B. Tiksnadi 106 D Coronary artery disease (CAD) remains a major global health problem. The high morbidity and mortality rates of this disease require simple screening parameters to assess future prognosis. One of the new parameters that can assess the prognosis is UAR. Uric acid plays a role in the infl ammatory process in the cardiovascular system, while albumin has the opposite role as an anti-infl ammatory and antioxidant. Uric acid can increase infl ammatory responses through the activation process of nucleotide-binding oligomerization domain-like receptors (NLRs) [26, 27]. A study by Rugerrio et al. showed an association between uric acid levels and increased markers of infl ammation [28]. Infl ammatory conditions are closely related to the incidence of cardiovascular disease and may represent one of the parameters that predict worse outcomes [29, 30]. Hyperuricemia may be considered a risk factor for heart failure and a parameter in the prognosis of higher cardiovascular mortality [31, 32]. Infl ammation also plays a role in the pathophysiology of CAD [33]. Infl ammatory conditions are closely related to the process of coronary collateral formation (CCC) in patients with CAD. Several studies have reported that CCC formation is correlated with several infl ammatory parameters [34, 35, 36]. In CAD patients, good CCC has the function of reducing infarct area to preserve myocardial function [37, 38]. People with poorer coronary collaterals also have a higher tendency to develop acute coronary occlusion [39]. Therefore, CAD patients with good CCC formation have a better prognosis. This meta-analysis study showed that patients with good CCC had lower UAR values. The study by Toprak et al. showed similar results to this study with an AUC value of 0.733, 95% CI 0.685 to 0.781, p-value < 0.01, and when compared with other infl ammatory parameters such as C-reactive protein to albumin ratio (CAR), neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), systemic immune-infl ammation index (SSI), and monocyte to high-density lipoprotein cholesterol ratio (MHR), UAR was superior to all of them. UAR was also superior to both uric acid and albumin [24]. In the population of CAD patients with CTO, UAR can also be used as a parameter to detect CCC formation [22]. In the setting of non-ST elevation myocardial infarction (NSTEMI) cases, UAR also has the ability to predict CCC conditions and is superior when compared to uric acid or albumin separately and C-reactive protein (CRP) [40]. In addition to its association with CCC, UAR is also a parameter for determining the severity of CAD and NSTEMI [12, 41]. In patients who have undergone percutaneous coronary intervention (PCI), UAR may predict long-term mortality and in-stent restenosis [42, 43]. In the setting of ST elevation myocardial infarction (STEMI), UAR has a role in predicting the incidence of no-refl ow and new-onset atrial fi brillation [44, 45]. The role of UAR as a novel parameter in coronary artery disease and myocardial infarction needs to be further explored with a larger sample size, considering its role as a novel infl ammatory marker has been performed very well in several previous studies. This meta-analysis study could not show the cut-off value of UAR in predicting coronary collateral events. This is because the included albumin parameters had diff erent types of testing methods. However, the study by Saylik et al. showed a cut-off value of 1.32 to detect poor CCC with a sensitivity of 72% and a specifi city of 83%. Another study by Toprak et al. also showed a not too diff erent cut-off point of 1.62 with a sensitivity of 70% and a specifi city of 71% [22, 24]. This study did not evaluate the relationship between poor collaterals and occurrence of ACS, so patients with good collaterals may still develop ACS, but patients with good collaterals tend to have a higher survival rate. 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