U-shaped relationship between fasting blood glucose concentration and gensini sore analysis in STEMI patients

doi:10.21203/rs.3.rs-3123814/v1

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U-shaped relationship between fasting blood glucose concentration and gensini sore analysis in STEMI patients

https://doi.org/10.21203/rs.3.rs-3123814/v1

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Background: An association between fasting blood glucose (FBG) and gensini scores has been reported. However, no studies have investigated the relationship between ST elevation myocardial infarction and FBG.

The purpose of this study was to investigate the association between FBG and coronary artery disease severity score (Gensini score) in patients with ST-segment elevation (STEMI) myocardial infarction.

Methods: In a retrospective analysis of 464 enrolled patients, we used minimum absolute contraction and selection operator (lasso) regression analysis to screen for covariates; In multiple regression analyses, we used gensini scores as the dependent variable, glucose as the independent variable, Fasting blood glucose was divided into hypoglycemic group 5.30mmol/L (2.69-6.15), medium glycemic group 7.11mmol/L (6.19-8.61) and hyperglycemic group 10.70mmol/L (8.64-14.81)，and selected variables as covariates to adjust and observe the true association between glucose and gensini scores. Considering that there is not necessarily a linear relationship between blood glucose and Guernsini score in the real world, we used curve fitting to observe the changing trend of blood glucose and Guernsini score.

Results: When confounding factors are not adjusted, The gensini scores in the medium-glycemic and high-glycemic groups had significant clinical significance (the medium-glycemic group, 95%CI: -140162--0.213, P=0.044; Hyperglycemia group, 95%CI: 5.295-19.221, P=0.0006;) The relationship between fasting blood glucose and Guernsini score is U-shaped but non-linear. Using minimum absolute contraction and selection operator (LASSO) regression to select variables, Variables included neutrophils, hemoglobin, platelets, albumin, low-density lipoprotein, D-dimer, urea nitrogen, glucose, white blood cells, systolic blood pressure, heart rate, age, dm, culprit vessel, history of myocardial infarction, Killip grade, number of stents, creatinine, uric acid, total cholesterol,CTNI, CKMB, left ventricular end-diastolic diameter, left atrial diameter. After adjusting for confounder factors, only the hyperglycemic group had significant clinical significance in Gensini score (95%CI: -16.95--2.71, P=0.015). Curve fitting analysis showed that fasting glucose and Gensini score presented a U-shaped but non-linear relationship in patients diagnosed with or without diabetes.

Conclusions: The severity of coronary stenosis in patients with ST-segment elevation myocardial infarction is influenced by either excessively high or excessively low fasting glucose concentration,Whether people with or without diabetes.

Acute myocardial infarction

Gensini socre

Fasting blood glucose

St elevation (STEMI) myocardial infarction

diabetes mellitus (DM)

Acute myocardial infarction (AMI) is a coronary artery occlusion or severe narrowing due to the formation of a clot in atherosclerotic plaque.[1].it is estimated that more than 7 million people worldwide are diagnosed with AMI each year[2].AMI has a high mortality rate[3]; Myocardial infarctions with ST-segment elevation (STEMI) are more prevalent, Among major coronary adverse events, it is one of the leading causes of mortality and morbidity[4].Cardiovascular morbidity and mortality are significantly increased by diabetes mellitus (DM), Especially in patients with acute coronary syndrome (ACS), myocardial infarction (MI), or unstable angina pectoris. There is no difference in prognosis between CAD patients without myocardial infarction and CAD patients with myocardial infarction without DM[5].According to the existing research results, In patients with poor blood glucose control, coronary heart disease risk status progressively deteriorates. Fasting hyperglycemia first detected after myocardial infarction is associated with poor prognoses[6], Using it is able to provide a more accurate assessment of coronary heart disease prognosis and predict the subsequent development of diabetes[7]. gensini score is mainly an evaluation index reflecting the severity of coronary artery lesions. The increase of FBG is correlated with Gensini score[8, 9].Previous studies have shown a significant positive correlation between fasting glucose and gensini scores, confirming even in the non-diabetic range[10–13].It has been found in the past that there is a positive linear correlation between blood glucose and GENSINI score. Only the influence of blood glucose on GENSINI is considered, without considering the influence of other factors such as uric acid, creatinine and age on GENSINI.Previous meta-analyses and the original article used a single truncation to distinguish high-risk hyperglycemia[14], however,both hypoglycemia and hyperglycemia are high-risk factors for AMI.

Therefore, in our study, three fasting glucose cutoff values were used and the effects of other variables((such as uric acid, creatinine, age, etc.)) were adjusted to assess the association with gensini scores.This study focused on the relationship between FBG and coronary artery disease severity score (Gensini score) in patients with St-segment elevation (STEMI) myocardial infarction.

Study participants

All data used in this research can be downloaded freely and without restriction from Dryad Digital Repository (https://datadryad.org). Data from: predictive value of apelin-12 in patients with ST-elevation myocardial infarction with different renal function: a prospective observational study,Dryad,Dataset, https://datadryad.org/stash/dataset/ doi:10.5061/dryad.m484p.Briefly,From January 2010 to October 2014, 464 patients underwent PCI in the First People's Hospital of Taizhou, Zhejiang Province, China.Current research is the secondary analysis based on a previous study, thus it is not necessary to provide participants informed consent and approval by the ethics committee.Exclusion criteria included non-STEMI; Balloon angioplasty alone; Severe vascular heart disease; Cardiogenic shock; Secondary hypertension; Patients with a history of cerebrovascular attack (within 1 year) or cerebrovascular development accompanied by significant residual neurological impairment; Salvage PCI; Previous ventricular fibrillation; Non-pci conservative treatment; A history of chronic hepatitis or cirrhosis; Untreated third or advanced atrioventricular block; Estimated life expectancy. 12 months; Endocrine diseases such as thyroid dysfunction or adrenocortical dysfunction; Severe renal insufficiency requiring dialysis; Recent severe infection, connective tissue disease; Malignant tumor; Known contraindications for statins, heparin, aspirin, clopidogrel, contrast agents, or glycoprotein IIb/IIia inhibitors (GPI); Gastrointestinal or urogenital tract bleeding obvious; Major surgery or trauma within 6 weeks with incomplete clinical data. Active severe bleeding.

Statistical analysis

In this study, participants were divided into three groups according to the level of blood glucose(tertile 1: 2.69–6.15 mmol/L; tertile 2: 6.19–8.61 mmol/L and tertile 3: 8.64–14.81 mmol/L), and the differences of gensini score between the three groups were compared. The assumption ofnormality of distribution was examined using Shapiro-Wilk test. For continuous data with normal distribution, variables are represented as mean ± standard deviation, and least significant difference (LSD) variance analysis was conducted to evaluate the differences between groups. For continuous data with nonnormal distribution, variables are represented as median(min-max), and Kruskal Wallis was conducted to evaluate the differences between groups. For categorized data, variables are represented as number and percentages (n, %) and chi-square test was conducted to evaluate the differences between groups. In univariate analysis, we use gensini score as dependent variable and other variables as independent variable to explore the relationship between them. In addition, in order to better investigate the association between blood glucose and gensini score, we used the least absolute shrinkage and selection operator (lasso) regression analysis to screen covariates. Furthermore, we used gensini score as dependent variable, glucose as independent variable, and selected variables as covariates in multivariate regression analysis to adjust and observe the real association between glucose and gensini score. Considering that there is not necessarily a linear relationship between blood glucose and gensini score in the real world, we use curve fitting to observe the change trend of blood glucose and gensini score. In this study, we used R version 3.6.1 (https://www.r-project.org/) for statistical analysis. P < 0.05 indicated that there was statistical difference (two sides).

Baseline patient characteristics were included

In Table 1, 464 subjects were divided into three group: tertile 1, 5.30 mmol/L(2.69–6.15), tertile 2, 7.11mmol/L(6.19–8.61), and tertile 3, 10.70mmol/L (8.64–14.81). The baseline characteristics of the three groups are listed. These three groups were compared in terms of anatomic, hematological characteristics and demographic. There were significant differences in total cholesterol,gensini score (p < 0.001).Overall baseline characteristics are presented in Table 2. The mean age of the subjects was 63 (63.00 ± 11.92) years; The mean fasting blood glucose of all patients was 7.67 (7.67 ± 2.53) mmol/L. 355 males and 109 females were included. There were 314 patients without diabetes and 150 patients with diabetes.

The KILLIP grade of the patient was 1–2, and the culprit vessels of coronary artery due to myocardial infarction were mainly 1 or 3 vessels.

In a single factor analysis,gensini scores were associated with hemoglobin, low-density lipoprotei, D-dime, blood glucos, leukocyte, Systolic blood pressur, myocardial infarction histor, creatinine, uric acid, total cholesterol, CTN, CKM, left ventricular end-diastolic diameter and left atrial diameter were significantly correlated (P < 0.05). FBG was positively correlated with gensini score(r = 2.0,P<0.0007).

Main analysis results

In Figure 1,fasting blood glucose was divided into hypoglycemic group 5.30mmol/L (2.69-6.15), medium glycemic group 7.11mmol/L (6.19-8.61) and hyperglycemic group 10.70mmol/L (8.64-14.81), and there were clinically significant differences among the three different subgroups(P＜0.05). When the variables were not adjusted (see Table 3), gensini scores in the moderate glycemic group and the hyperglycemic group had significant clinical significance (the moderate glycemic group, 95%CI:-140162--0.213,P=0.044; Hyperglycemia group, 95%CI:5.295-19.221, P=0.0006) The relationship between fasting blood glucose and gensini score was U-shaped, but non-linear. Minimum absolute contraction and selection operator (LASSO) regression was used to Select variables included in the model2 model, and the screening criteria for variables was as follows: select lambda = lambdam.min: 0.8132 (-0.2068). Screening of variables including neutrophils, hemoglobin, platelet, albumin, low density lipoprotein, d dimer, urea nitrogen, glucose, white blood cells , systolic blood pressure, heart rate , age, dm, culprit vessels , myocardial infarction history, Killip grade, stent number, creatinine, uric acid, total cholesterol, ctni, ckmb, left ventricular end diastolic diameter,left atrial diameter.After adjusting for confounding factors, only the hyperglycemic group had significant clinical significance with gensini score (95%CI:-16.95--2.71,P=0.015), Curve fitting analysis revealed a U-shaped but non-linear relationship between fasting glucose and Gensini score in patients without or without diabetes diagnosis (FIG.2).

It is a common cause of death for diabetic patients to suffer from myocardial infarction (MI)[15]. There are relevant studies that show hypoglycemia and hyperglycemia on admission are associated with higher in-hospital mortality, The relationship between fasting blood glucose (FBG), admitted blood glucose and mean hospitalized blood glucose and in-hospital mortality was U-shaped or J-shaped[14, 16–18]. Park et al. found a J-shaped relationship between fasting glucose levels and the risk of atherosclerotic cardiovascular disease[19].Yuhan Qin et al. reviewed 958 consecutive AMI patients undergoing emergency coronary angiography, and found that FBG was positively correlated with Gensini score (r = 0.171,P< 0.001)[10].Tong Zhao et al. reviewed 64 patients with chest discomfort accompanied by coronary angiography; In patients with diabetes (r = 0.312,P<0.012) and non-diabetic patients (r = 0.387, P<0.010), FBG was positively correlated with Gensini score[11].Jingjing Jiang et al included 1852 patients with coronary angiography for analysis, and the result showed that FBG was positively correlated with Gensini score (r = 0.09,P<0.01), regression analysis showed that FBG was independently correlated with Gensini score[12].In 85 patients with acute coronary syndrome who underwent emergency coronary angiography and implanted stents when necessary, FPG was positively correlated with Gensini score (r = 0.568,P<0.000)[8].In 906 patients who underwent coronary angiography and had no history of diabetes, a significant positive correlation was found between FPG and Gensini score (r = 0.172; P = 0.011)[13].Previous studies used single truncation method to distinguish high-risk hyperglycemia[14], Only considering the effect of blood glucose on gensini score,but both hypoglycemia and hyperglycemia are high-risk factors for AMI. In our study, we used three fasting glucose thresholds, hypoglycemic group 5.30mmol/L (2.69–6.15), medium glycemic group 7.11mmol/L (6.19–8.61) and hyperglycemic group 10.70mmol/L (8.64–14.81).And adjusted for other variables (e.g. neutrophils, hemoglobin, platelet, albumin, low density lipoprotein, d dimer, urea nitrogen, glucose, white blood cells, systolic blood pressure, heart rate, age, dm, culprit vessels, myocardial infarction history, Killip grade, stent number, creatinine, uric acid, total cholesterol, ctni, ckmb, left ventricular end diastolic diameter, left atrial diameter) to assess the correlation with gensini scores.In our population-based study,We found that fasting blood glucose and gensini score showed a U-shaped relationship rather than a linear relationship.

Hyperglycemia can cause damage to the microvascular endothelial barrier and promote the development of atherosclerosis[20].

There appears to be a main mechanism involved in endothelial dysfunction involving the protein kinase C (PKC) pathway. Increased non-enzymatic glycosylation, increased oxidative stress and decreased endothelial insulin action[21].However, vascular injury caused by high glucose concentration is associated with activation of nuclear factor κB(NFκB) and overexpression of many genes in endothelial cells, mononuclear macrophages and vascular smooth muscle cells. Proteins and lipids are subjected to high levels of glucose for a prolonged period of time, resulting in advanced glycation end products (AGEs)[22]. Reactive oxygen species can be generated by AGEs, and it has been shown that sugar oxidation can damage tissues in the local microenvironment of artery walls, leading to diabetic macrovascular damage[23]. Reactive oxygen species (ROS) are overproduced in cardiovascular cells of diabetic patients, and activation of intercellular adhesion molecule-1 (ICAM-1) leads to atherosclerosis progression[24, 25].Fasting blood glucose is a better predictor of mortality and even fatal heart failure than admission blood glucose.Previous studies have shown that fasting blood glucose is more advantageous than admitted blood glucose in predicting 30-day mortality in non-diabetic AMI patients[26] and fasting blood glucose is a better predictor of long-term mortality. There was a 3.9-fold increase in mortality among non-diabetic AMI patients with admitted blood glucose levels above 110 mg/dl (6.11 mmol/L) versus patients with normal blood glucose levels[27].Patients admitted with elevated blood glucose did not face increased risks when their fasting glucose was normal, Moreover, when admitted to hospital with normal blood sugar, elevated fasting blood glucose triples mortality[28].

Both hypoglycemia and hyperglycemia are associated with cardiovascular disease.The risk of cardiovascular events increases with hypoglycemia, and severe hypoglycemia may directly affect cardiovascular outcomes and mortality[29]. Those with hypoglycemia experienced stent thrombosis and infarct-related arterial occlusions (2–8 days after STEMI) at higher rates[30, 31]. Hypoglycemia contributes to the development of cardiovascular disease in several ways, A variety of physiological reactions take place, including vasoconstriction, white blood cell activation, dysfunction of the autonomic nervous system, and release of inflammatory mediators and cytokines[32].It has been shown that hypoglycemia increases the concentration of inversely regulating hormones (namely endogenous catecholamine, glucagon, cortisol, and growth hormone)[33], Changes in the concentration of various hormones can lead to increases in myocardial contractility and myocardial oxygen demand, leading to associated cardiovascular effects[30]. Hypoglycemia can trigger an inflammatory cascade; Hypoglycemia is associated with increased platelet aggregation, which promotes thrombosis and promotes the development of coronary atherosclerosis[34]. Patients with hypoglycemia are at increased risk of subsequent adverse cardiovascular events based on systematic reviews and meta analyses (including more than 325,000 hospitalized patients). Studies have shown that hypoglycemia is significantly correlated with cardiovascular adverse events in critically ill patients and non-critically ill patients, diabetic patients and non-diabetic patients, individuals with mild hypoglycemia and severe hypoglycemia, as well as long-term to short-term follow-up studies[35].

In conclusion, both high and low blood sugar can have an impact on the cardiovascular system and lead to an increased risk of cardiovascular events.In our study of St-segment elevation (STEMI) myocardial infarction patients, We observed a U-shaped relationship between fasting blood glucose and gensini score, and a U-shaped relationship between fasting blood glucose and Gensini score in myocardial infarction patients with or without diabetes, meeting the adverse effects of both hyperglycemia and hypoglycemia on the cardiovascular system.Our study still has some limitations. First, we only studied STEMI patients, so it is unclear whether the relationship between blood glucose and gensini score analyzed can be extrapolated to other cases. Second, the blood glucose concentration selected for our analysis was 2.69-14.81mmol/L. However, the relationship between concentrations lower than 2.69mmol/L and higher than 14.81mmol/L is still unclear. Third, relatively few cases were included in the study, which may affect the results, and further verification is still needed in larger studies.

Conflicts of interest

There are no conflicts of interest reported by the authors.

Acknowledgments

Thanks to the Cardiac Medicine Collaborative Group for their support. The General Project of Guangxi Natural Science Foundation (82060068) provided support.

Ethics Approval and Consent to Participate

The study was a secondary analysis based on previous research, so informed consent and approval from an ethics committee was not required to be provided to participants.

Availability of data and materials

Conflicts of interest

All authors declare no competing interests

Contribution

All the data and content of this paper were written by Li Han, the first author, and Zhong Guoqiang is the corresponding author.

Acknowledgments

A special thanks to the Cardiac Medicine Collaborative Group for their support.

Funding/Support

No project funding is available

Consent for publication

Not applicable

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Tables 1 to 3 are available in the Supplementary Files section.

No competing interests reported.

Table1.xlsx
Table 1 Characteristics of the study participants by fasting blood glucose tertiles.
Table2.xlsx
Table 2. Univariate analysis related to GENSINI score.
Table3.xlsx
Table 3. Curve fitting model of FBG and Gensini scores. Model 1, Unadjusted model. Model 2,Adjusted for all covariates:Neutrophils, hemoglobin, platelet, albumin, low density lipoprotein, d dimer, urea nitrogen, glucose, white blood cells, systolic blood pressure, heart rate, age,dm, culprit vessels, myocardial infarction history, Killip grade, stent number, creatinine, uric acid, otal cholesterol, tctni, ckmb,left ventricular end diastolic diameter and left atrial diameter.

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Version 1

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U-shaped relationship between fasting blood glucose concentration and gensini sore analysis in STEMI patients

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Version 1

Abstract

Figures

Introduction

A description of the methodology and materials

Study participants

Statistical analysis

Result

Baseline patient characteristics were included

Discussion

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1