Association of haemoglobin glycation index with outcomes in patients with acute coronary syndrome: results from an observational cohort study in China

Baseline characteristics of patients

The baseline characteristics of the enrolled patients in different HGI groups are illustrated (Table 1). The median HGI of the 11004 patients was −0.196 (−7.188, 7.875). The five quintiles are −0.906 (−7.188, −0.663), −0.491 (−0.663, −0.343), −0.196 (−0.342, −0.039), 0.170 (−0.039, 0.485), and 1.156 (0.485, 7.875), respectively. In the Q2-Q3 groups, the prevalence of diabetes, dyslipidaemia, and stroke were significantly lower than those in the Q1, Q4 and Q5 groups. The systolic blood pressure and medication usage on admission (antiplatelet agents, ACEIs/ARBs, beta-blockers, and statins) increased with the HGI levels. In addition, BMI, HbA1c, and FPG are positively associated with HGI. Male patients are more likely to have a lower HGI.

Table 1 Baseline characteristics of the study populationHGI predicted the occurrence of MACCEs

The incidence of composite MACCEs was calculated (Table 2). MACCEs occurred in 3298 (30.0%) patients [784 (7.1%) all-cause death, 420 (3.8%) CV death, 457 (4.2%) nonfatal MI, 164 (1.5%) nonfatal stroke, 2638 (24.0%) cardiac rehospitalization, 739 (6.7%) revascularizations]. Low and high HGI leaded to increased risk of all-cause death, CV death, and composite MACCEs significantly increased along with HGI levels (p < 0.001), while patients with moderate HGI (Q2: −0.491 (−0.663, −0.343)) presented the lowest rate of the above outcomes. During the median of 36.5 months of follow-up, Kaplan–Meier analysis of event-free survival indicated that there was a significant difference of survival rate among HGI groups (Figs. 3 and 4). Cox regression analyses and predictors for subvarieties of MACCEs is presented (Table 3). Univariate analysis found that the predictors associated with MACCEs occurrence were HGI, age, hypertension, diabetes, previous stroke/MI, past PCI/CABG, BMI, blood pressure, heart rate, diagnosis with NSTEMI, UA, angiotensin-converting enzyme inhibitor/angiotensin receptor blocker (ACEI/ARB) usage at admission, laboratory data including WBC, haemoglobin, Hs-CRP, RBG at admission, FPG, HbA1c, albumin, creatinine, eGFR, TC, LDL-C, HDL-C, left ventricular ejection fraction, LM/three-vessel or proximal LAD involved, medication during hospitalization including antiplatelet agents, ACEI/ARB and statins, alpha-glucosidase inhibitor and insulin usage (P < 0.05). After adjusting for confounding factors, multivariate Cox proportional hazards regression analysis indicated that age, hypertension, previous stroke, past PCI, BMI, heart rate, NSTEMI, WBC, eGFR, HDL-C, LVEF, LM/three-vessel or proximal LAD involved, and antiplatelet agents during hospitalization independently predicted the incidence of MACCEs in ACS patients. Finally, competing risk regression analysis was employed to compare the endpoints in different groups. The results indicate that the cumulative occurrence of CV death, nonfatal MI, revascularization, and nonfatal MACCEs were significantly correlated with HGI levels on unadjusted competing risk modelling. Notably, after adjusting for confounding factors, the multivariate-adjusted hazard ratio (HR) also increased with increasing HGI for CV death (P < 0.05). It is reported that patients with HGI of Q2-Q3 may suffer the lowest incidence of CV death and nonfatal stroke [CV death: Q2: 0.547 (0.403–0.742); Q3: 0.466 (0.340,0.640); nonfatal stroke: Q2: 0.512 (0.305,0.860); Q3: 0.625 (0.387,1.011)] (Table 4). To further investigate this issue, RCS were employed to analyze the relationship between HGI and the incidence of MACCEs. An HGI between −1.32 and 0.12 positively impacted the composite MACCEs after adjusting for confounding factors (χ2 = 12.7, P = 0.005) (Fig. 5). Similar results were also found for all-cause death (HGI between −1.32 and 0.46) (χ2 = 25.3, P < 0.001) and CV death (HGI between −1.32 and −0.08) (χ2 = 11.9, P = 0.008) (Additional file 1: Figure S1).

Table 2 Clinical outcomesFig. 3figure 3

Kaplan–Meier curves for composite MACCEs of the the five quintiles. MACCEs major adverse cardiac and cerebral events, HGI haemoglobin glycation index

Fig. 4figure 4

Kaplan–Meier curves for all-cause death (A), CV death (B), non-fatal MI (C), cardiac rehospitalization (D), revascularization (E), non-fatal stroke (F) of the the five quintiles. CV death, cardiovascular death; MI, myocardial infarction

Table 3 Independent predictors of composite MACCEsTable 4 Competing risk model of clinical outcomesFig. 5figure 5

Unadjusted and adjusted RCS of HGI and the incidence of composite MACCEs. Adjusted model included age, BMI, heart rate, hypertension, previous stroke, past PCI, NSTEMI, WBC, eGFR, HDL-C, LVEF, LM/three-vessel or proximal LAD involved, and antiplatelet agents during hospitalization. RCS restricted cubic spline, HGI haemoglobin glycation index, HR hazard ratio, MACCEs major adverse cardiac and cerebral events, BMI body mass index, PCI percutaneous coronary intervention, NSTEMI non-ST segment elevation myocardial infarction, WBC white blood cells, eGFR estimated glomerular filtration rate, HDL-C high-density lipoprotein cholesterol, LVEF left ventricular ejection fraction, LM left main vessel, LAD left anterior descending artery

Independent association of HGI with MACCEs in different subgroups

Subgroup analysis was carried out according to age, sex, BMI, smoker, hypertension, diabetes, eGFR, and LVEF, demonstrating a predictive effect of HGI on MACCEs in many subgroups (Fig. 6). For patients aged ≥ 65 years, moderate HGI (Q2, Q3, Q4) usually comes with a lower incidence of MAACEs. Male patients with Q2 HGI and female patients with Q2-Q4 HGI suffered a lower risk of MACCEs. For patients with BMI ≥ 25 and hypertension, HGI within Q2-Q3 was correlated with a lower incidence of MACCEs. Patients with LVEF < 55 or without diabetes had a lower risk of MACCEs in the Q1-Q4 HGI groups than in the Q5 HGI group.

Fig. 6figure 6

Forest plot of composite MACCEs according to different subgroups. Adjusted model included age, BMI, heart rate, hypertension, previous stroke, past PCI, NSTEMI, WBC, eGFR, HDL-C, LVEF, LM/three-vessel or proximal LAD involved, and antiplatelet agents during hospitalization. HR hazard ratio, MACCEs major adverse cardiac and cerebral events, BMI body mass index, PCI percutaneous coronary intervention, NSTEMI non-ST segment elevation myocardial infarction, WBC white blood cells, eGFR estimated glomerular filtration rate, HDL-C high-density lipoprotein cholesterol, LVEF left ventricular ejection fraction, LM left main vessel, LAD left anterior descending artery, Ref. reference(Q5, 0.485 ≤ HGI < 7.875)

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