Value of glycemic dispersion index in predicting major adverse cardiovascular events in diabetic patients with concomitant acute coronary syndrome

doi:10.21203/rs.3.rs-3602905/v1

Download PDF

Research Article

Value of glycemic dispersion index in predicting major adverse cardiovascular events in diabetic patients with concomitant acute coronary syndrome

https://doi.org/10.21203/rs.3.rs-3602905/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Objective: This investigation aims to assess the predictive value of the glycemic dispersion index (GDI), calculated by incorporating glycated hemoglobin, fasting plasma glucose, and 2-hour postprandial plasma glucose, in predicting major adverse cardiovascular events (MACE) within a 12-month timeframe for diabetic patients with concomitant acute coronary syndrome (ACS).

Methods A total of 3261 diabetic patients diagnosed with ACS were selected from the Department of Cardiology, the Sixth Affiliated Hospital of Kunming Medical University, between January 2016 and July 2022. A thorough screening process was conducted, resulting in the inclusion of 512 patients based on the predefined exclusion criteria. Comprehensive clinical data and laboratory examination indicators were collected for all included patients. Subsequently, a follow-up was carried out for 12 months, with the last follow-up being concluded on July 31, 2023. During this period, the occurrence of MACE was meticulously recorded for each enrolled patient. Based on the GDI calculated for the enrolled patients, they were divided into quartile groups. A comparative analysis was performed to evaluate the clinical data and laboratory indicators among these groups. Additionally, the incidence of MACE in each quartile group was calculated. To further identify the risk factors associated with MACE in diabetic patients with concomitant ACS, COX proportional hazards regression analysis and Kaplan-Meier survival analysis were conducted. Finally, the cumulative risk of MACE over time was compared among the different study groups.

Results The patients included in the study were sorted into four groups, Q1, Q2, Q3, and Q4, according to their GDI levels, from lowest to highest. The respective proportions of MACE in the Q1 to Q4 groups were 10.16%, 12.50%, 15.63%, and 16.41%. GDI, STEMI, TC, TG, LDL, and hs-CRP independently predicted the hazards for MACE in diabetic individuals with ACS. The cumulative risk of MACE over time was considerably more significant in those with a GDI > 4.21 than those with a GDI ≤ 4.21.

Conclusion The elevated GDI is correlated with an augmented risk of MACE in diabetic patients with concomitant ACS, thereby serving as an early indicator for assessing the unfavorable clinical prognosis of patients.

Diabetes

Acute coronary syndrome

Major adverse cardiovascular events

Glycemic dispersion index

Glycemic variability

On December 6, 2021, the International Diabetes Federation released the "2021 IDF Global Diabetes Atlas (10th edition)", stating that approximately 537 million people with diabetes aged 20–79 globally. It is estimated that about 6.7 million adults die from diabetes or its complications [1]. Cardiovascular disease is the most common cause of death among individuals with diabetes, accounting for 52% of deaths in individuals with type 2 diabetes and 44% in those with type 1 diabetes [2]. Meanwhile, ACS represents a highly critical acute clinical subtype among them. ACS exhibits significant clinical variations, and even among patients discharged from the hospital after excluding ACS based solely on cardiac biomarkers and electrocardiography, there is still a 3–5% occurrence of myocardial infarction. The mortality rate for ACS patients within one month of discharge is 8.6%, which increases to 13.3% within one year [3]. Therefore, it is crucial to predict the risk of MACE in diabetic patients with concomitant ACS at an early stage and to promptly and accurately screen high-risk patients. Despite the incomplete understanding of the pathogenesis of ACS, research findings suggest that the hyperglycemic state and insulin resistance in diabetic patients lead to increased oxidative stress, triggering intracellular molecular signaling. The ensuing prothrombotic and inflammatory mediators accelerate the formation and rupture of atherosclerotic plaques in the coronary arteries [4]. However, epidemiological investigations have revealed that the SYNTAX score for coronary artery disease is independently associated with elevated glycemic variability. High-frequency and high-amplitude glycemic variability are related to the severity of coronary artery disease [5]. The foundational experiments have also confirmed that high-frequency and high-amplitude glycemic variability have a more detrimental effect on coronary arteries than chronic persistent hyperglycemia [6]. Further investigations have indicated that glycemic variability not only exacerbate endothelial cell damage by enhancing oxidative stress and triggering inflammatory responses [7], but are also closely associated with plaque burden, thus promoting endothelial injury and plaque rupture in coronary arteries [8]. High-frequency and high-amplitude glycemic variability are one of the important risk factors leading to the occurrence of MACE in diabetic patients with concomitant ACS. Therefore, besides monitoring traditional risk factors such as blood pressure, blood glucose, and lipid levels, monitoring glycemic variability in diabetic patients with concomitant ACS is an essential approach to improve screening efficiency and early prevention of MACE occurrences.

Although clinical studies have confirmed that the standard deviation of blood glucose levels and the average amplitude of glycemic variability are strong independent predictors of the risk of MACE in diabetic patients [9, 10], However, it remains challenging for diabetic patients to lower the occurrence of MACE through monitoring glycemic variability, the main reason is that the current mainstream methods of monitoring glycemic variability are continuous glucose monitoring devices and self-blood glucose monitoring, which are both complex to operate, expensive, and involve complex evaluation indicators [11, 12], these factors have resulted in low clinical utilization rates of continuous glucose monitoring devices and poor compliance with self-blood glucose monitoring methods among elderly diabetic patients [13], furthermore, a significant portion of diabetic patients have not received effective monitoring of their glycemic variability. Therefore, in our previous research, our research group calculated a clinically applicable screening index for abnormal glycemic variability called the glycemic dispersion index (GDI) by combining glycated hemoglobin A1c (HbA1c), fasting plasma glucose (FPG) and 2-hour postprandial plasma glucose (2hPG). Previous research conducted by our research group has confirmed the high screening efficiency of this index for abnormal glycemic variability [14]. Moreover, compared to traditional methods for monitoring glycemic variability, this index is more accessible to operate and more suitable for regular monitoring of glycemic variability in diabetic patients. Although this index can be a simplified alternative to traditional glycemic variability evaluation indicators, it remains unclear whether it can act as an independent predictor for the risk of MACE in diabetic patients with concomitant ACS. Therefore, this study conducted a 12-month follow-up on 512 diabetic patients with concomitant ACS to evaluate whether the GDI has predictive value for the risk of MACE occurrence in this patient population.

Study subjects

Study population: 3261 diabetic patients with concomitant ACS who were hospitalized in the Department of Cardiology of the Sixth Affiliated Hospital of Kunming Medical University from January 2016 to July 2022 were selected as study participants. All patients met the diagnostic criteria for ACS recommended in the 2016 ACC/AHA guidelines [15] and the diagnostic and classification criteria for diabetes released by the American Diabetes Association in 2018 [16]. A total of 2749 patients were excluded based on the exclusion criteria ( Fig. 1): (1) 2579 patients with missing data on HbA1c, FPG, or 2hPG; (2) 25 patients diagnosed with severe valve diseases or myocardial diseases; (3) 34 patients with concomitant malignant tumors; (4) 68 patients with CKD stages G3a-G5; and (5) 43 patients lost to follow-up during the study. The final follow-up was completed on July 31, 2023, through telephone interviews, clinical visits, and hospital records for all enrolled patients.

Clinical data and laboratory indicators

The patient's gender, age, smoking history, history of hypertension, history of myocardial infarction/stroke, history of percutaneous coronary intervention (PCI)/coronary artery bypass grafting (CABG), body mass index (BMI) measured on admission, systolic blood pressure (SBP), diastolic blood pressure, heart rate (HR), and any clinical symptoms of acute coronary syndrome occurring 24 hours before admission, including ST-elevation myocardial infarction (STEMI), non-ST-segment elevation myocardial infarction (NSTEMI), and unstable angina (UA), were collected through the patient's medical records on admission.

The FPG, HbA1c, 2hPG, alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine (CREA), total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), creatine kinase-MB (CK-MB), high-sensitivity troponin T (hs-cTnT), homocysteine (HCY), N-terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity C-reactive protein (hs-CRP), hemoglobin (HGB), white blood cells (WBC), and red blood cells (RBC) detected after the patient's admission was recorded.

GDI formula and study subject grouping

The patients' FPG, 2hPG, and HbA1c values are inputted into the following formula to calculate the GDI value for each patient. Based on the GDI levels from low to high, the patients are grouped into quartiles, namely Q1, Q2, Q3, and Q4 groups. According to the GDI cut-off value of 4.21, they are further divided into GDI ≤ 4.21 group and GDI > 4.21 group.

GDI=\(\sqrt{{(FPG-eAG)}^{2}+{(2ℎPG-eAG)}^{2}}\)

eAG_mmol/l=1.59×HAb_1c%-2.59

Follow-up of study subjects

Following discharge, the enrolled patients were followed up, and the occurrence of MACE within 12 months was recorded. MACE includes all-cause mortality (cardiac and non-cardiac), non-fatal myocardial infarction, non-fatal stroke (ischemic and hemorrhagic), cardiogenic shock, heart failure, and coronary revascularization.

Statistical analysis

Continuous variables are presented as mean ± standard deviation or median [Q1, Q3], while categorical variables are presented as n%. Essential demographic characteristics among groups were compared using ANOVA, Kruskal-Wallis test, and Pearson chi-square test. COX proportional hazards regression analysis was employed to identify risk factors for the occurrence of MACE, and Kaplan-Meier survival analysis was used to analyze the cumulative risk of MACE among different GDI groups. A p-value < 0.05 was considered statistically significant. The statistical analysis was performed using SPSS Statistical Software 24.0.

Comparison of patient clinical data and laboratory indices

The number of individuals in the MACE and non-MACE groups is 70 and 442, respectively. The GDI values of patients in the MACE group were higher than those in the non-MACE group (Figure 2). The median values of GDI for the MACE and non-MACE groups were 5.43 (3.39-7.89) and 4.49 (2.96-6.51), respectively. The study subjects were divided into four groups (Q1, Q2, Q3, Q4) based on ascending GDI values. The proportions of MACE occurrence in Q1 to Q4 groups are 10.16%, 12.50%, 15.63%, and 16.41%, respectively. The differences in GDI, SBP, DBP, WBC, hs-CRP, and FPG among the four groups were statistically significant (P<0.05). The hs-CRP and FPG values of patients in the Q4 group were significantly higher than those in the Q1 to Q3 groups, while there was no significant trend in SBP, DBP, and WBC levels among the groups. The between-group differences in other clinical data were not statistically significant (P>0.05) (Table 1).

*Table 1 Comparison of patient clinical data and laboratory indices*
Variable	Total population	Quartile 1	Quartile 2	Quartile 3	Quartile 4	p value
Variable	n=512	n=128	n=128	n=128	n=128	p value
GDI(range)	4.49（2.97-6.59）	2.05（1.52-2.64）	3.81（3.45-4.20）	5.38（4.96-5.83）	8.56（7.47-9.96）	0.000
Age( years)	62.19±10.78	61.75±10.44	61.82±11.48	61.78±10.23	63.85±10.56	0.443
Physical examination
BMI(kg/m²)	24.78±9.29	24.88±2.67	26.34±18.16	24.05±2.82	23.83±2.92	0.132
HR(bpm)	81.22±15.42	81.87±16.18	79.91±14.57	80.03±14.13	82.11±16.10	0.386
SBP(mmHg)	131.58±22.70	132.45±22.19	129.21±23.48	127.91±22.47	136.16±21.49	0.026
DBP(mmHg)	80.76±14.41	82.06±14.54	77.87±13.22	79.56±14.31	83.08±15.29	0.017
Laboratory data
TC(mmol/L)	4.27±1.18	4.32±1.09	4.11±1.14	4.34±1.16	4.36±1.31	0.383
TG(mmol/L)	1.68(1.12-2.67)	1.79(1.26-2.96)	1.71(1.12-2.69)	1.63(1.12-2.55)	1.62(1.02-2.84)	0.790
HDL(mmol/L)	1.06±0.28	1.04±0.26	1.04±0.28	1.07±0.27	1.07±0.32	0.728
LDL(mmol/L)	2.48±0.94	2.51±0.90	2.32±0.96	2.54±0.91	2.60±1.00	0.222
CK-MB(U/L)	2.46(1.54-4.32)	2.51(1.48-4.37)	2.20(1.48-3.63)	2.34(1.54-4.27)	3.00(1.68-5.03)	0.787
NT-proBNP(pg/mL)	666.80(202.40-1718.50)	579.70(187.60-1447.75)	562.20(151.95-1480.00)	700.95(223.33-1469.50)	1011.00(310.43-2882.00)	0.115
WBC(×10⁹)	8.70±3.17	8.66±3.16	8.81±3.21	8.30±3.00	8.86±3.06	0.000
RBC(×10¹²)	4.59±0.61	4.62±0.64	4.57±0.61	4.62±0.56	4.61±0.58	0.253
HGB(g/L)	126.38±44.16	131.60±38.81	124.28±47.63	126.32±45.66	124.42±45.18	0.953
Hs-CRP( mg/L)	4.47(1.57-19.05)	4.21(1.50-13.33)	3.20(1.07-27.71)	4.17(1.52-14.31)	8.02(2.27-29.47)	0.013
ALT(u/L)	30.00(19.00-48.00)	32.00(21.00-48.00)	28.50(19.00-45.75)	26.50(18.00-47.00)	28.00(16.75-51.00)	0.557
AST(u/L)	42.00(22.00-133.00)	43.50(24.00-128.75)	37.00(20.00-115.00)	43.50(21.00-132.25)	44.00(24.00-171.00)	0.384
CREA(umol/L)	77.00(62.00-99.00)	74.50(59.75-99.00)	77.00(62.25-95.00)	76.00(64.00-90.75)	79.50(63.50-114.25)	0.266
hs-cTnT(ng/L)	0.71(0.04-2.44)	0.71(0.02-2.63)	0.48(0.15-2.41)	0.96(0.17-2.25)	0.79(0.07-2.76)	0.326
HCY(umol/L)	14.80(12.40-17.90)	14.80(12.10-18.33)	14.80(12.53-16.60)	14.45(12.25-18.38)	14.8(12.55-18.40)	0.603
FPG(mmol/L)	10.38±4.34	9.19±2.90	9.62±3.41	10.37±3.87	12.11±5.59	0.000
Clinical presentation
STEMI(n%)	51.37	53.91	47.66	56.25	47.66	0.397
NSTEMI(n%)	19.73	21.09	26.56	14.84	16.41	0.080
UA(n%)	28.90	25.00	25.78	28.91	35.94	0.200
Medical history
Previous Stroke，(n%)	53.71	56.25	53.13	55.47	50.00	0.704
Hypertension(n%)	32.42	31.25	28.91	39.06	30.47	0.308
PCI/CABG(n%)	87.11	82.03	87.50	90.63	88.28	0.210
Previous MI/Stroke(n%)	79.49	74.22	78.91	84.38	80.47	0.245
parameter are presented as mean ± standard deviation (SD), median [Q1, Q3], or percentage. Abbreviations: GDI, glycemic dispersion index; BMI, body mass index; HR, heart rate; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglycerides; HDL, high-density lipoprotein; LDL, low-density lipoprotein; CK-MB, creatine kinase-MB; NT-proBNP: N-terminal pro-B-type natriuretic peptide; WBC, white blood cell count; RBC, red blood cell count; HGB, hemoglobin; hs-CRP, high-sensitivity C-reactive protein; ALT, alanine aminotransferase; AST, aspartate aminotransferase; CREA, creatinine; hs-cTnT, high-sensitivity cardiac troponin T; HCY, homocysteine; FPG, fasting plasma glucose; STEMI, ST-elevation myocardial infarction; NSTEMI, non-ST-elevation myocardial infarction; UA, unstable angina; PCI, percutaneous coronary intervention; CABG, coronary artery bypass graft.

Risk factor analysis for the occurrence of MACE within 12 months in diabetic patients with concomitant ACS

A univariate analysis using COX proportional hazards regression revealed that risk factors associated with MACE included Hs-CRP, admission clinical symptoms of STEMI, and UA (P<0.05). After adjusting for confounding factors, the multivariate Cox proportional hazards regression analysis showed that GDI, STEMI, TC, TG, LDL, and Hs-CRP independently predicted the risk of MACE occurrence in diabetic patients with concomitant ACS (P<0.05) (Table 2). Kaplan-Meier survival analysis was utilized to compare the occurrence of endpoint events during the follow-up period among patients in the Q1 to Q4 groups. The analysis results indicated that, as time progressively extended, the cumulative risk of MACE in the Q3 and Q4 groups steadily surpassed that of the Q1 and Q2 groups (Figure 3A). Based on previous studies, the cut-off value of the GDI for screening abnormal glycemic variability was determined to be 4.21. The study subjects were divided into two groups: a GDI≤4.21 group and a GDI＞4.21 group. Kaplan-Meier survival analysis revealed that the cumulative risk of MACE was significantly higher in the GDI＞4.21 group compared to the GDI≤4.21 group (Log-rank χ2=4.406, P=0.036) (Figure 3B).

Table 2 Independent predictors of MACE
Variable	Univariate HR (95% CI)	p value	Multivariate Adjusted HR (95% CI)	p value
GDI(range)	1.059(0.992-1.131)	0.087	1.133(1.055-1.216)	0.001
Age(years)	0.996(0.975-1.018 )	0.741
Physical examination
BMI(kg/m2)	0.999(0.972-1.027)	0.929
HR(bpm)	0.993(0.977-1.008)	0.338
SBP(mmHg)	1.005(0.995-1.015)	0.316
DBP(mmHg)	1.011(0.996-1.027)	0.660
Laboratory data
TC(mmol/L)	0.978(0.797-1.199)	0.827	2.064(1.283-3.321)	0.003
TG(mmol/L)	1.005(0.936-1.080)	0.883	0.838(0.728-0.965)	0.014
HDL(mmol/L)	1.333(0.590-3.010)	0.480
LDL(mmol/L)	0.811(0.624-1.055)	0.119	0.364(0.205-0.648)	0.001
CK-MB(U/L)	1.001(0.995-1.007)	0.693
NT-proBNP(pg/mL)	1.000(1.000-1.000)	0.353
WBC(×10⁹)	0.972(0.899-1.051)	0.476
RBC(×10¹²)	1.101(0.749-1.619)	0.624
HGB(g/L)	0.996(0.992-1.001)	0.113
ALT(u/L)	0.991(0.981-1.002)	0.104
AST(u/L)	0.999(0.997-1.001)	0.273
CREA(umol/L)	0.999(0.996-1.002)	0.497
hs-cTnT(ng/L)	1.000(1.000-1.000)	0.965
HCY(umol/L)	0.986(0.956-1.018)	0.392
FPG(mmol/L)	0.961(0.906-1.020)	0.191	0.943(0.885-1.005)	0.069
Clinical presentation
STEMI(n%)	1.643(1.011-2.667)	0.045	2.166(1.038-3.587)	0.003
NSTEMI(n%)	1.035(0.576-1.859)	0.908
UA(n%)	0.487(0.262-0.907)	0.023
Medical history
Previous Stroke(n%)	0.953(0.595-1.525)	0.840
Hypertension(n%)	1.172(0.719-1.910)	0.525
Past PCI/CABG(n%)	0.874(0.478-1.596)	0.661
Previous MI/Stroke(n%)	0.858(0.411-1.791)	0.683
Abbreviations: GDI, glycemic dispersion index; BMI, body mass index; HR, heart rate; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglycerides; HDL, high-density lipoprotein; LDL, low-density lipoprotein; CK-MB, creatine kinase-MB; NT-proBNP: N-terminal pro-B-type natriuretic peptide; WBC, white blood cell count; RBC, red blood cell count; HGB, hemoglobin; hs-CRP, high-sensitivity C-reactive protein; ALT, alanine aminotransferase; AST, aspartate aminotransferase; CREA, creatinine; hs-cTnT, high-sensitivity cardiac troponin T; HCY, homocysteine; FPG, fasting plasma glucose; STEMI, ST-elevation myocardial infarction; NSTEMI, non-ST-elevation myocardial infarction; UA, unstable angina; PCI, percutaneous coronary intervention; CABG, coronary artery bypass graft.

The China Cardiovascular Disease Care Collaborative (CCC) project has shown that the prevalence of diabetes and prediabetes among patients with ACS is as high as 37.6%. Furthermore, ACS patients with comorbid diabetes or prediabetes have a 1.5-fold increased risk of MACE and a 2-fold increased risk of all-cause mortality [17]. The 2017 European Society of Cardiology (ESC) Guidelines for the management of ST-elevation myocardial infarction (STEMI) [18] and the 2020 ESC Guidelines for the management of non-ST-elevation acute coronary syndromes (NSTE-ACS) [19] both recommend risk stratification of patients with Acute Myocardial Infarction (AMI). Early risk stratification is of significant importance for selecting optimal secondary prevention medications. Currently, the primary risk model for predicting MACE in patients with ACS is the Global Registry of Acute Coronary Events (GRACE) score [20]. However, there is currently no risk assessment model designed explicitly for predicting MACE in ACS patients with comorbid diabetes. Additionally, when the GRACE score is used in the diabetic population, it lacks laboratory markers that reflect inflammation, oxidative stress, and other pathophysiological aspects related to metabolic diseases [20], and its predictive accuracy is insufficient to provide timely and accurate clinical evidence for drug intervention. Therefore, it is of great significance to identify objective indicators that reflect the prognosis of diabetic patients with concomitant ACS and utilize them to screen the risk of MACE occurrence, and this will provide meaningful guidance for clinical treatment and improve patient outcomes.

Research shows that in the diabetic population, high-frequency and high-amplitude glycemic variability enhance oxidative stress, triggering an intensified inflammatory response that exacerbates endothelial damage within blood vessels. This damage to the coronary endothelium may even be more severe than the damage caused by persistent hyperglycemia [5–8]. The results of the study conducted by Lim S [21] suggest that higher glycemic variability is a significant risk factor for MACE in diabetic patients. Sungha Park [22] found that higher glycemic variability during hospitalization was associated with all-cause mortality within one year in diabetic patients with comorbid cardiovascular disease.

Therefore, based on previous research findings, the GDI has an Area Under the Curve (AUC) of 0.901 (95% CI, 0.856–0.945) for screening abnormal glycemic variability, with a sensitivity of 0.781, specificity of 0.905, and a cut-off value of 4.21 [14], our research team believes that the GDI demonstrates good efficacy in screening abnormal glycemic variability and may serve as a predictive factor for the risk of MACE in diabetic patients with concomitant ACS.

In this study, the GDI values of patients in the MACE group were analyzed and compared to those in the non-MACE group. It was observed that the median, upper quartile and lower quartile of the GDI values in the MACE group were higher than those in the non-MACE group. Subsequently, the study population was divided into four groups based on the ascending order of their GDI values, ranging from low to high. The incidence of MACE gradually increased from the first group to the fourth group, indicating a progressive rise in MACE occurrence with increasing GDI values. Multivariable COX proportional hazards regression was used to investigate whether GDI is a risk factor for MACE in diabetic patients with concomitant ACS. Results revealed that GDI, STEMI, TC, TG, LDL, and Hs-CRP could independently predict the risk of MACE occurrence in diabetic patients with concomitant ACS.

Moreover, GDI, STEMI, and TC are identified as risk factors for the occurrence of MACE. However, the study results showed that TG, LDL, and Hs-CRP are protective factors, which is inconsistent with conventional beliefs. In the studies conducted by Subin Lim [21] and Marjo Okkonen [23], an inverse relationship between plasma lipids and the risk of MACE occurrence was also observed. Some scholars believe that this may be related to higher cardiovascular disease prevention and treatment awareness among patients with elevated plasma lipid levels. Our research group believes that although the lipid infiltration theory has been recognized as an essential mechanism in the development of coronary artery disease, the widespread use of lipid-lowering drugs has made plasma lipid levels no longer reflect the actual lipid metabolism status of patients. Therefore, coronary heart disease risk assessment models such as China-PAR Project [24] and UK QRISK [25] do not include any plasma lipid-related laboratory parameters but only consider "the use of lipid-lowering drugs" as an assessment factor. These phenomena may be influenced by lipid substances such as Small Dense LDL [26] and Remnant Lipoprotein Cholesterol [27] that have been proposed in recent years, the specific mechanisms behind these effects require further in-depth research in the future.

Regarding the analysis of Hs-CRP as a protective factor for MACE in this study, our research group believes that although Hs-CRP is a recognized cardiovascular disease biomarker. Many clinical studies consider it as an independent risk factor for cardiovascular disease. Previous studies have focused on healthy individuals or sub-healthy individuals who have previously experienced ACS but were asymptomatic at the time of inclusion in the study cohorts [28]. In contrast, the subjects of this study are diabetes patients admitted for ACS occurrence. Research reports have shown that Hs-CRP levels in ACS patients are significantly higher than those in stable angina patients and healthy individuals [29]. Hs-CRP peaks on the second day after the occurrence of ACS and is much higher than the reference value. Moreover, the relative variability of hs-CRP levels within one year after ACS occurrence can reach up to 206.1% [30]. Therefore, the Hs-CRP levels detected in this study may be considerably higher than those of patients in non-stressful states, resulting in a contradiction between the research findings of Hs-CRP in this study and traditional beliefs. It is worth noting that recent research has revealed the high variability of hs-CRP even in asymptomatic and clinically stable ACS patients [30], factors such as obesity, short-term temperature fluctuations, genetic polymorphism of the angiotensinogen gene, as well as the expression levels of miR−27a and miR−329 in peripheral blood monocytes, can all influence hs-CRP levels [31–34], therefore, taking individual differences into account, continuous periodic monitoring of hs-CRP values and evaluating them based on the mean level of hs-CRP may potentially yield more accurate assessment results in cardiovascular disease risk assessment.

In this study, GDI was identified as a simple screening index for abnormal glycemic variability. It was considered the leading risk factor for the occurrence of MACE in diabetic patients with concomitant ACS in multivariate COX proportional hazards regression. In Kaplan-Meier survival analysis, within the follow-up period, the cumulative risk of MACE occurring within 12 months in the high GDI group gradually exceeds that of the low GDI group. Furthermore, the abnormal GDI group demonstrates a significantly faster increase in the cumulative risk of MACE occurrence within 12 months than the normal GDI group, further confirming the close association between GDI and the risk of MACE occurrence in diabetes patients with concomitant ACS.

In summary, when the GRACE score, Hs-CRP, and traditional lipid parameters are not suitable for evaluating the risk of MACE occurrence in diabetes patients, the level of glycemic variability serves as an important indicator reflecting the pathological levels of oxidative stress, inflammation, and other factors within the bodies of diabetes patients, close monitoring of this level can help improve the prognosis of diabetes patients with concomitant ACS. In this study, the GDI has been confirmed as an independent predictor for the occurrence of MACE within 12 months in diabetes patients with concomitant ACS. Additionally, monitoring GDI values is simple, cost-effective, and demonstrates high clinical applicability. Therefore, in clinical practice, paying attention to the GDI value of patients, along with their clinical symptoms and physiological and biochemical indicators, and conducting a comprehensive assessment of the risk of MACE occurrence in diabetes patients with concomitant ACS is crucial, early implementation of primary and secondary preventive treatments holds significant importance in improving patient prognosis.

Limitations of this study include: (1) being a single-center observational study, which requires further expansion of the sample size and the conduct of multi-center studies to validate the reliability of the results and the predictive threshold; (2) solely recording the GDI indicators of patients upon admission, without conducting dynamic monitoring of GDI throughout the patient's treatment course.

Acknowledgements

All authors would like to thank all collaborators of this study

Author contributions

Shi R, Feng L, and Xu WB were involved in study design, researched data, contributed to discussion, and wrote/edited the manuscript. Ye D and Luo BB performed the statistical analyses and wrote/edited the manuscript. Liu YM, Cao HY and Tang LT contributed to discussions and reviewed/edited the manuscript. All authors approved the final version of the manuscript.

Funding

Supported by the National Natural Science Foundation of China [Project No. 82160402]

Special Fund for Training Leading Medical Talents in Yunnan Province, China [Project No. L-2019022]

Kunming Medical University Joint Special Key Project [Project No. 202301AY070001-024]

Availability of data an

The datasets used and/or analyzed in the current study are available from the

corresponding author upon reasonable request.

Ethics approval

This study was approved by the ethics committee of the Sixth Affiliated Hospital of Kunming Medical University [approval number 2022kmykdx6f154] and was exempt from obtaining informed consent. The study was conducted following the ethical requirements of the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Author details

1 Clinical Laboratory, Yan’an Hospital of Kunming City, Kunming City, Yunnan Province, People’s Republic of China;

2 Department of Laboratory, The Sixth Affiliated Hospital of Kunming

Medical University, Yuxi City, Yunnan Province, People’s Republic of China

3 Department of Laboratory Medicine, The Sixth Affiliated Hospital of

Guangzhou Medical University, Qingyuan People’s Hospital, Qingyuan,

People’s Republic of China

Internationales diabetes federation.IDF Diabetes Atlas 2021.https://diabetesatlas.org/
Morrish NJ,Wang SL,Stevens LK,Fuller JH,Keen H. Mortality and causes of death in the WHO Multinational Study of Vascular Disease in Diabetes. Diabetologia. 2001;44(2):S14-S21.https://doi.org/10.1007/pl00002934.
Panduranga P,Sulaiman KJ,Al-Zakwani I,Alhabib KF,Hersi A,Suwaidi JA,Alsheikh-Ali AA,Almahmeed W,Saif SA,Al-Faleh H,Al-Lawati J,Asaad N,Al-Motarreb A,Amin H. Acute coronary syndrome in indian subcontinent patients residing in the middle east: results from gulf registry of acute coronary events II. Angiology. 2018;66(9):818-825.https://doi.org/10.1177/0003319714556812.
Huang D,Refaat M,Mohammedi K,Jayyousi A,Al Suwaidi J,Abi Khalil C. Macrovascular Complications in Patients with Diabetes and Prediabetes. Biomed Res Int. 2017;2017:7839101.https://doi.org/10.1155/2017/7839101.
Benalia M,Zeller M,Mouhat B,Guenancia C,Yameogo V,Greco C,Yao H,Maza M,Vergès B,Cottin Y. Glycaemic variability is associated with severity of coronary artery disease in patients with poorly controlled type 2 diabetes and acute myocardial infarction. Diabetes Metab. 2019;45(5):446-452.https://doi.org/10.1016/j.diabet.2019.01.012.
Wu N,Shen H,Liu H,Wang Y,Bai Y,Han P. Acute blood glucose fluctuation enhances rat aorta endothelial cell apoptosis, oxidative stress and pro-inflammatory cytokine expression in vivo. Cardiovasc Diabetol. 2018;15(1):1-13.https://doi.org/10.1186/s12933-016-0427-0.
Zhang ZY,Miao LF,Qian LL,Wang N,Qi MM,Zhang YM,Dang SP,Wu Y,Wang RX. Molecular mechanisms of glucose fluctuations on diabetic complications. Front Endocrinol (Lausanne). 2019;10:640.https://doi.org/10.3389/fendo.2019.00640.
Zhang T,Su G,Mi SH,Yang HX,Xin W,Dai WL,Liu JH. Association Between Blood Glucose Variability and the Characteristics of Vulnerable Plaque in Elderly Non-ST Segment Elevation Acute Coronary Syndrome Patients. Int Heart J. 2019;60(3):569-576.https://doi.org/10.1536/ihj.18-503.
Gerbaud E,Darier R,Montaudon M,Beauvieux MC,Coffin-Boutreux C,Coste P,Douard H,Ouattara A,Catargi B. Glycemic variability is a powerful independent predictive factor of midterm major adverse cardiac events in patients with diabetes with acute coronary syndrome. Diabetes Care. 2019;42(4):674-681.https://doi.org/10.2337/dc18-2047.
Takahashi H,Iwahashi N,Kirigaya J,Kataoka S,Minamimoto Y,Gohbara M,Abe T,Okada K,Matsuzawa Y,Konishi M,Maejima N,Hibi K,Kosuge M,Ebina T,Tamura K,Kimura K. Glycemic variability determined with a continuous glucose monitoring system can predict prognosis after acute coronary syndrome .Cardiovasc Diabetol. 2018;17:116.https://doi.org/10.1186/s12933-018-0761-5.
Danne T,Nimri R,Battelino T,Bergenstal RM,Close KL,DeVries JH,Garg S,Heinemann L,Hirsch I,Amiel SA,Beck R,Bosi E,Buckingham B,Cobelli C,Dassau E,Doyle FJ,Heller S,Hovorka R,Jia W,Jones T,Kordonouri O,Kovatchev B,Kowalski A,Laffel L,Maahs D,Murphy HR,Nørgaard K,Parkin CG,Renard E,Saboo B,Scharf M,Tamborlane WV,Weinzimer SA,Phillip M. International consensus on use of continuous glucose monitoring. Diabetes care. 2017;40(12):1631-1640.https://doi.org/10.2337/dc17-1600.
Onisie O,Crocket H,de Bock M. The CGM grey market: a reflection of global access inequity. Lancet Diabetes Endocrinol. 2019;7(11):823-825.https://doi.org/10.1016/S2213-8587(19)30263-3.
Li XL.Research Progress on Factors Affecting Blood Glucose Monitoring Compliance and Nursing in Elderly Patients with Diabetes. Electronic Journal of Practical Clinical Nursing Science. 2018;3(7):16-21.https://doi.org/10.3969/j.issn.2096-2479.2018.07.015.
Shi R,Feng L,Liu YM,Xu WB,Luo BB,Tang LT,Bi QY,Cao HY. Glycemic dispersion: a new index for screening high glycemic variability. Diabetol Metab Syndr. 2023;15(1):95.https://doi.org/10.1186/s13098-023-01077-y.
Levine GN,Bates ER,Blankenship JC,Bailey SR,Bittl JA,Cercek B,Chambers CE,Ellis SG,Guyton RA,Hollenberg SM,Khot UN,Lange RA,Mauri L,Mehran R,Moussa ID,Mukherjee D,Ting HH,O'Gara PT,Kushner FG,Ascheim DD,Brindis RG,Casey DE,Chung MK,de Lemos JA,Diercks DB,Fang JC,Franklin BA,Granger CB,Krumholz HM,Linderbaum JA,Morrow DA,Newby LK,Ornato JP,Ou N,Radford MJ,Tamis-Holland JE,Tommaso CL,Tracy CM,Woo YJ,Zhao DX. 2015 ACC/AHA/SCAI Focused Update on Primary Percutaneous Coronary Intervention for Patients With ST-Elevation Myocardial Infarction: An Update of the 2011 ACCF/AHA/SCAI Guideline for Percutaneous Coronary Intervention and the 2013 ACCF/AHA Guideline for the Management of ST-Elevation Myocardial Infarction: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Society for Cardiovascular Angiography and Interventions. Circulation. 2016;133(11):1135-47.https://doi.org/10.1161/CIR.0000000000000336.
American Diabetes Association. 2.Classifcation and diagnosis of diabetes: Standards of Medical Care in Diabetes-2018. Diabetes Care. 2018;41(Suppl.1):S13-S27.https://doi.org/10.2337/dc18-S002.
Metabolic Cardiovascular Disease Group of the Cardiovascular Disease Branch of the Chinese Medical Association,Editorial Board of the Chinese Journal of Cardiovascular Diseases.Chinese expert consensus on comprehensive management of cardiovascular risk in patients with cardiovascular diseases and abnormal glucose metabolism. Chinese Journal of Cardiology. 2021;49(7):656-672.https://doi.org/10.3760/cma.j.cn112148-20210114-00050.
Ibanez B,James S,Agewall S,Antunes MJ,Bucciarelli-Ducci C,Bueno H,Caforio ALP,Crea F,Goudevenos JA,Halvorsen S,Hindricks G,Kastrati A,Lenzen MJ,Prescott E,Roffi M,Valgimigli M,Varenhorst C,Vranckx P,Widimský P. 2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: The Task Force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC). Eur Heart J. 2018;39(2):119-177.https://doi.org/10.1093/eurheartj/ehx393.
Collet JP,Thiele H,Barbato E,Barthélémy O,Bauersachs J,Bhatt DL,Dendale P,Dorobantu M,Edvardsen T,Folliguet T,Gale CP,Gilard M,Jobs A,Jüni P,Lambrinou E,Lewis BS,Mehilli J,Meliga E,Merkely B,Mueller C,Roffi M,Rutten FH,Sibbing D,Siontis GCM. 2020 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation. Eur Heart J. 2021;42(14):1289-1367.https://doi.org/10.1093/eurheartj/ehaa575.
Shuvy M,Beeri G,Klein E,Cohen T,Shlomo N,Minha S,Pereg D. Accuracy of the Global Registry of Acute Coronary Events (GRACE) Risk Score in Contemporary Treatment of Patients With Acute Coronary Syndrome. Can J Cardiol. 2018;34(12):1613-1617.https://d oi.org/10.1016/j.cjca.2018.09.015.
Subin Lim,Se Hwa Chung,Ju Hyeon Kim,Yong Hyun Kim,Eung Ju Kim,Hyung Joon Joo. Effects of metabolic parameters' variability on cardiovascular outcomes in diabetic patients. Cardiovasc Diabetol. 2023;22(1):114.https://doi.org/10.1186/s12933-023-01848-x.
Sungha Park,Jaewon Oh,Sungha Park,Jin Joo Park,Sang Eun Lee,Minseok Kim,Hyun-Jai Cho,Jin-Oh Choi,Hae Young Lee,Kyung-Kuk Hwang,Kye Hun Kim,Byung-Su Yoo,Dong-Ju Choi,Sang Hong Baek,Eun-Seok Jeon,Jae-Joong Kim,Myeong Chan Cho,Shung Chull Chae,Byung-Hee Oh,Seok-Min Kang. In-hospital glycemic variability and all-cause mortality among patients hospitalized for acute heart failure. Cardiovasc Diabetol. 2022;21(1):291.https://doi.org/10.1186/s12933-022-01720-4.
Marjo Okkonen,Aki S. Havulinna,Olavi Ukkola,Heikki V. Huikuri,Arto Pietilä,Heli Koukkunen,Seppo Lehto,Juha Mustonen,Matti Ketonen,Juhani Airaksinen,Y. Antero Kesäniemi,Veikko Salomaa. Risk factors for major adverse cardiovascular events after the first acute coronary syndrome. Ann Med. 2021;53(1):817-823.https://doi.org/10.1080/07853890.2021.1924395.
Fangchao Liu,Jianxin Li,Jichun Chen,Dongsheng Hu,Ying Li,Jianfeng Huang,Xiaoqing Liu,Xueli Yang,Jie Cao,Chong Shen,Ling Yu,Zhendong Liu,Xianping Wu,Liancheng Zhao,Xigui Wu,Dongfeng Gu,Xiangfeng Lu. Predicting lifetime risk for developing atherosclerotic cardiovascular disease in Chinese population: The China-PAR project. Sci Bull (Beijing). 2018;63(12):779-787.https://doi.org/10.1016/j.scib.2018.05.020.
Hippisley-Cox J,Coupland C,Brindle P. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ. 2017;357:j2099.https://doi.org/10.1136/bmj.j2099.
Alyann Otrante,Abdelghani Bounafaa,Hicham Berrougui,Abdelkhalid Essamadi,Michel Nguyen,Tamas Fulop,Abdelouahed Khalil. Small Dense LDL Level and LDL/HDL Distribution in Acute Coronary Syndrome Patients. Biomedicines. 2023;11(4):1198-1198.https://doi.org/10.3390/biomedicines11041198.
Chen YY,Lue ZB,Zhao X.Correlation of Discordance between Remnant Lipoprotein Cholesterol and Non-high-density Lipoprotein Cholesterol and Severity of Coronary Stenosis. Chinese General Practice. 2023;26(24):3010-3015.https://doi.org/10.12114/j.issn.1007-9572.2022.0812.
Denegri A,Boriani G. High Sensitivity C-reactive Protein (hsCRP) and its Implications in Cardiovascular Outcomes. Curr Pharm Des. 2021;27(2):263-275.https://doi.org/10.2174/1381612826666200717090334.
Yang Y,Li G,Zhang R. Correlation Analysis of Acute Coronary Syndrome with Serum IL-18, MMP-9, hs-CRP, and Plasma FIB. Biomed Res Int. 2022;2022(0):1-9.https://doi.org/10.1155/2022/5984184.
Victor J.van den Berg,Victor Umans,Milos Brankovic,Rohit M.Oemrawsingh,Folkert W. Asselbergs,Pim van der Harst,Imo E. Hoefer,Bas Kietselaer,Harry J.G.M. Crijns,Timo Lenderink,A.J. Oude Ophuis,Ron H.N. van Schaik,Isabella Kardys,Eric Boersma,K. Martijn Akkerhuis. Stabilization patterns and variability of hs-CRP, NT-proBNP and ST2 during 1 year after acute coronary syndrome admission: results of the BIOMArCS study. Clin Chem Lab Med. 2020;58(12):2099-2106.https://doi.org/10.1515/cclm-2019-1320.
Xinyan Bi,Yi Ting Loo,Shalini Ponnalagu,Christiani Jeyakumar Henry. Obesity is an independent determinant of elevated C-reactive protein in healthy women but not men. Biomarkers. 2019;24(1):64-69.https://doi.org/10.1080/1354750x.2018.1501763.
Yuting Kang,Haosu Tang,Linlin Jiang,Su Wang,Xin Wang,Zuo Chen,Linfeng Zhang,Congyi Zheng,Zengwu Wang,Gang Huang,Runlin Gao. Air temperature variability and high-sensitivity C reactive protein in a general population of China. Sci Total Environ. 2020;749:141588.https://doi.org/10.1016/j.scitotenv.2020.141588
Min Zhu,Jun Lin,Chen Wang,Minjun Yang,Haiyan Lv,Mengqi Yang,Baohui Xu,Xiaofeng Chen,Jianjun Jiang. The relationship among angiotensinogen genes polymorphisms and hs-CRP and coronary artery disease. J Clin Lab Anal. 2019;33(5):e22881.https://doi.org/10.1002/jcla.22881.
Ali Rafiei,Gordon A. Ferns,Reza Ahmadi,Arsalan Khaledifar,Tina Rahimzadeh-Fallah,Mina Mohmmad-Rezaei,Shohreh Emami,Nader Bagheri. Expression levels of miR-27a, miR-329, ABCA1, and ABCG1 genes in peripheral blood mononuclear cells and their correlation with serum levels of oxidative stress and hs-CRP in the patients with coronary artery disease. IUBMB Life. 2021;73 (1):223-237.https://doi.org/10.1002/iub.2421.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Value of glycemic dispersion index in predicting major adverse cardiovascular events in diabetic patients with concomitant acute coronary syndrome

Status:

Version 1

Abstract

Figures

Introduction

Research design and methods

Study subjects

Clinical data and laboratory indicators

GDI formula and study subject grouping

GDI=\(\sqrt{{(FPG-eAG)}^{2}+{(2ℎPG-eAG)}^{2}}\)

Follow-up of study subjects

Statistical analysis

Result

Discussion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1