Prognostic Impact of Atherogenic Index of Plasma (AIP) In Type 2 Diabetes Mellitus Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention

doi:10.21203/rs.3.rs-92600/v1

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Prognostic Impact of Atherogenic Index of Plasma (AIP) In Type 2 Diabetes Mellitus Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention

https://doi.org/10.21203/rs.3.rs-92600/v1

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published 16 Nov, 2020

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Background: The association of atherogenic index of plasma (AIP), an emerging lipid index which can predict risk for cardiovascular (CV) disease, with adverse outcomes in type 2 diabetes mellitus (T2DM) patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI) has been undetermined. Therefore, the aim of this study was to investigate whether AIP could independently predict adverse CV events in T2DM patients with ACS undergoing PCI.

Methods: This study was a retrospective analysis of a single-centre prospective registry involving 826 consecutive T2DM patients who underwent primary or elective PCI for ACS at our CV center from June 2016 to November 2017. This study eventually included 798 patients (age, 61±10 years, male, 72.7%). AIP was calculated as the base 10 logarithm of the ratio of plasma concentration of triglycerides to high density lipoprotein-cholesterol (HDL-C). All patients were divided into 4 groups based on the AIP quartiles. The primary endpoint was a composite of all-cause death, non-fatal ischemic stroke, non-fatal myocardial infarction (MI), or unplanned repeat revascularization. The key secondary endpoint was a composite of cardiovascular death, non-fatal ischemic stroke, or non-fatal MI.

Results: During a median follow-up period of 927 days, 198 patients developed at least one event. An unadjusted Kaplan–Meier analysis showed the incidence of the primary endpoint increased gradually with rising AIP quartiles (log-rank test, P =0.001). A multivariate Cox proportional hazards analysis revealed that compared with the lowest AIP quartile, the top AIP quartile was associated with significantly increased risk for the primary and key secondary endpoints (hazard ratio [HR]: 2.153; 95% confidence interval [CI]: 1.355 to 3.421; P =0.001, and HR: 2.613; 95% CI: 1.024 to 6.666; P =0.044, respectively). Inclusion of AIP quartiles in a baseline prediction model for the primary endpoint increased the Harrell’s C statistic from 0.697 to 0.707. More importantly, addition of AIP quartiles to the above model significantly improved the continuous net reclassification improvement (continuous NRI =19.1%, P <0.001).

Conclusions: A higher AIP value on admission was independently and strongly associated with adverse CV events in T2DM patients with ACS undergoing PCI.

Cardiac & Cardiovascular Systems

Endocrinology & Metabolism

Atherogenic index of plasma

Type 2 diabetes mellitus

Acute coronary syndrome

Percutaneous coronary intervention

Adverse cardiovascular events

Type 2 diabetes mellitus (T2DM) has been established as an important risk factor for adverse outcomes in patients with acute coronary syndrome (ACS) [1–3]; this may be due to a rapid development of atherosclerosis related to T2DM [4]. T2DM patients with ACS remain at a very high residual risk of adverse cardiovascular (CV) events and mortality, despite receiving percutaneous coronary intervention (PCI) and guideline-recommended first-line medication therapies [5–8]. Therefore, further risk assessment of such patients may help better determine prognosis and guide better medical management.

Dyslipidemia is a common comorbidity in T2DM and ACS patients. The dyslipidemic pattern in diabetic patients [mainly characterized by high levels of fasting and postprandial triglyceride-rich lipoproteins, reduced high density lipoprotein-cholesterol (HDL-C), normal or slightly elevated low density lipoprotein-cholesterol (LDL-C), and the appearance of small dense LDL-C particles] is somewhat different from that in non-diabetic patients [9]. Atherogenic index of plasma (AIP), readily calculated from the lipid profile as log₁₀ (triglycerides/HDL-C), is considered an alternative and simple marker of plasma atherogenicity based on an observed significant, positive relationship between AIP and cholesterol esterification rates in apoB-lipoprotein-depleted plasma (FER(HDL)), very low density lipoprotein particle size, remnant lipoprotein particle cholesterol, and LDL density, and inverse correlation of AIP with particle sizes of HDL and LDL [10–16]. As a consequence, we presume that AIP can better reflect the lipid characteristics of diabetic patients. Recently, AIP has been demonstrated to be associated with CV morbidity and mortality in general population and different patients groups [17–28]. However, the prognostic impact of AIP on admission among T2DM patients with ACS undergoing PCI has not been exclusively studied. We hypothesized that AIP on admission is an independent predictor of adverse CV events in T2DM and ACS patients who underwent PCI.

Study population

This study was a retrospective analysis of a single-centre prospective registry involving 826 consecutive T2DM patients who underwent primary or elective PCI for ACS at our CV center from June 2016 to November 2017 [29]. For the purpose of this study, patients with prior coronary artery bypass grafting, left ventricular ejection fraction (LVEF) <30%, cardiogenic shock, and renal failure requiring hemodialysis were excluded. We also excluded three patients because their follow-up data was missing, despite at least four attempts to contact them. A total of 798 patients were eventually included in the present analysis. None of these patients received medications specifically designed to raise HDL-C or lower triglycerides, such as niacin, fibrates, and omega-3 fatty acids, before admission and at discharge.

Measurement

The fasting plasma glucose (FPG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and triglycerides levels after admission were measured in the central laboratory of Beijing Anzhen Hospital. The Friedewald equation was used to calculate the low-density lipoprotein cholesterol (LDL-C) level. AIP was calculated as the base 10 logarithm of the ratio of plasma concentration of triglycerides to high density lipoprotein-cholesterol (HDL-C) [10]. Moreover, we used standard questionnaires to collect demographics, medical history and medication history data for all patients.

Follow-up and endpoints

The follow-up time points were one month and every six months after hospital discharge. Trained personnel who were not aware of patients' baseline data obtained information about adverse events via telephone contact with patients or their family members, using standardized questionnaires; adverse events were identified by carefully reviewing the corresponding medical records. The primary endpoint was a composite of all-cause death, non-fatal ischemic stroke, non-fatal myocardial infarction (MI), or unplanned repeat revascularization. The key secondary endpoint was a composite of cardiovascular death, non-fatal ischemic stroke, or non-fatal MI. Adverse events were defined in accordance with our previous publications [29, 30]. The follow-up period was ended in November 2019.

Statistical analysis

All patients were stratified into 4 groups (Q1 [AIP ≤0.0147], Q2 [0.0147< AIP ≤0.1850], Q3 [0.1850< AIP ≤0.3517] and Q4 [AIP >0.3517]) according to the AIP quartiles. Categorical variables were expressed as frequencies and percentages. Chi-squared test or Fisher’s exact test was used to determine the statistical significance of the differences in categorical variables between groups. Continuous variables with parametric distributions were expressed as the means ± standard deviations and those with nonparametric distributions were expressed as medians and interquartile ranges. Unpaired t-test or Mann-Whitney U test and Analysis of Variance or Kruskal-Wallis H test were used to estimate the statistical significance of differences in continuous variables between groups. Kaplan-Meier curves and Cox proportional hazards models were used to conduct survival analyses of the primary and key second endpoints. The differences between Kaplan-Meier estimates were evaluated by the log-rank test. Cox proportional hazards analysis results were presented using hazard ratios (HRs) with 95% confidence intervals (CIs). Variables of statistical significance and clinical importance were included in the multivariate Cox proportional hazards model. The AIP quartiles were added to the other independent predictors identified in the multivariate Cox proportional hazards analysis for the primary endpoint and Harrell’s C statistic was calculated; it is generally believed that Harrell’s C statistic above 0.7 is acceptable discriminatory power, above 0.8 is excellent discriminatory power, and above 0.9 is outstanding discriminatory power. The incremental effect of adding AIP quartiles to other independent predictors for the primary endpoint was assessed using the continuous net reclassification improvement (NRI). Post-hoc subgroup analyses stratified by age (<60 versus ≥60 years), sex (female versus male), body mass index (BMI) (<28 versus ≥28 kg/m²), LDL-C (≤1.8 versus >1.8 mmol/L), hypertension (yes versus no), and clinical presentation (non-ST versus ST segment elevation ACS) were used to determine the consistency of the prognostic impacts of AIP as a continuous variable for the primary endpoint. SPSS version 24.0 (IBM Corp., Armonk, New York, US) and R software version 3.5.3 (R Foundation for Statistical Computing, Beijing, China) were used for analyses. A 2-sided P-value <0.05 was considered significant.

A total of 798 T2DM patients with ACS undergoing PCI were included in the present analysis. The mean age of these patients was 61 ± 10 years, and male patients accounted for 72.7%. The baseline clinical and laboratory characteristics of the study population according to the AIP quartiles are listed in Table 1. Patients with higher AIP values tended to be younger, were predominantly male, had higher rates of current smoking and dyslipidaemia, and had lower rates of never smoking and diagnosis with unstable angina pectoris. Patients with higher AIP values were more likely to have higher levels of BMI, serum creatinine (SCr), uric acid, TC, LDL-C, triglycerides, and FPG, while have lower levels of pulse pressure (PP) and HDL-C. Use of medications, agiographic findings, and procedural results of the study population according to the AIP quartiles are summarized in Table 2. With the exception of β-blockers, medications before admission did not differ across the different AIP groups. With the exception of P2Y12 inhibitors, angiotensin converting enzyme inhibitors/angiotensin II receptor blockers (ACEIs/ARBs), and oral antidiabetic agents, medications at discharge were similar among the different AIP groups. Patients who had higher AIP values were more likely to receive ACEIs/ARBs at discharge. The proportions of left main/three-vessel disease and two-vessel disease were different among the different AIP groups. Patients with higher AIP values tended to have a higher rate of chronic total occlusions, and a lower rate of heavy calcification lesions. The proportions of left circumflex artery and right coronary artery interventions were different among the different AIP groups. Patients with higher AIP values tended to have a lower rate of complete revascularization.

Table 1

Baseline clinical and laboratory characteristics of the study population according to the AIP quartiles
Variable	Q1 n = 199	Q2 n = 200	Q3 n = 200	Q4 n = 199	P value
Demographics
Age (years)	63 ± 8	63 ± 10	61 ± 9	58 ± 12	< 0.001
Male sex, n (%)	125 (62.8)	146 (73.0)	153 (76.5)	156 (78.4)	0.002
Clinical values (on admission)
BMI (kg/m²)	25.3 ± 3.1	25.4 ± 2.8	26.1 ± 3.1	27.0 ± 3.5	< 0.001
SBP (mm Hg)	133 ± 18	133 ± 17	130 ± 16	131 ± 16	0.116
DBP (mm Hg)	75 ± 11	76 ± 10	75 ± 10	77 ± 11	0.203
PP (mm Hg)	58 ± 16	58 ± 15	55 ± 15	54 ± 14	0.015
Risk factors
Smoking status
Current smoking, n (%)	60 (30.2)	77 (38.5)	76 (38.0)	111 (55.8)	< 0.001
Former smoking, n (%)	33 (16.6)	33 (16.5)	38 (19.0)	26 (13.1)	0.455
Never smoking, n (%)	106 (53.3)	90 (45.0)	86 (43.0)	62 (31.2)	< 0.001
Chronically daily drinking, n (%)	14 (7.0)	18 (9.0)	26 (13.0)	21 (10.6)	0.234
Family history of CHD, n (%)	54 (27.1)	60 (30.0)	69 (34.5)	56 (28.1)	0.386
Hypertension, n (%)	138 (69.3)	137 (68.5)	141 (70.5)	130 (65.3)	0.715
Dyslipidaemia, n (%)	107 (53.8)	174 (87.0)	192 (96.0)	195 (98.0)	< 0.001
Previous MI, n (%)	36 (18.1)	43 (21.5)	40 (20.0)	51 (25.6)	0.301
Past PCI, n (%)	47 (23.6)	50 (25.0)	50 (25.0)	44 (22.1)	0.891
Previous ischemic stroke or TIA, n (%)	13 (6.5)	13 (6.5)	17 (8.5)	8 (4.0)	0.338
PAD, n (%)	27 (13.6)	29 (14.5)	31 (15.5)	35 (17.6)	0.713
Cardiac failure, n (%)	12 (6.0)	13 (6.5)	23 (11.5)	19 (9.5)	0.156
LVEF (%)	64 (60–67)	64 (60–68)	65 (60–68)	65 (59–68)	0.963
Clinical presentation
UAP, n (%)	171 (85.9)	153 (76.5)	150 (75.0)	153 (76.9)	0.033
NSTEMI, n (%)	19 (9.5)	28 (14.0)	28 (14.5)	25 (12.6)	0.493
STEMI, n (%)	9 (4.5)	19 (9.5)	22 (11.0)	21 (10.6)	0.088
Laboratory measurements (fasting state)
BUN (mmol/L)	5.2 (4.3–6.3)	5.4 (4.5–6.5)	5.4 (4.6–6.2)	5.5 (4.6–6.7)	0.173
SCr (µmol/L)	66.5 (59.3–73.6)	69.2 (61.9–80.3)	70.3 (62.2–81.3)	71.5 (63.2–82.2)	< 0.001
UA (µmol/L)	303.2 (271.7-347.2)	321.3 (279.6–373.0)	323.8 (282.5–386.0)	368.5 (307.0-410.3)	< 0.001
TC (mmol/L)	3.90 ± 0.96	4.06 ± 1.01	4.10 ± 0.99	4.47 ± 1.03	< 0.001
LDL-C (mmol/L)	2.23 ± 0.85	2.48 ± 0.84	2.48 ± 0.77	2.53 ± 0.76	0.001
HDL-C (mmol/L)	1.23 ± 0.22	1.04 ± 0.17	0.95 ± 0.17	0.87 ± 0.14	< 0.001
Triglycerides (mg/dl)	0.88 (0.72-1.00)	1.31 (1.18–1.48)	1.71 (1.51–1.99)	2.66 (2.25–3.38)	< 0.001
FPG (mg/dl)	6.80 (6.12–8.23)	7.17 (6.32–8.25)	6.80 (5.91–7.88)	7.65 (6.61–8.49)	< 0.001
Glycated haemoglobin (%)	7.0 (6.6–8.1)	7.3 (6.7–8.2)	7.2 (6.6–8.1)	7.3 (6.7-8.0)	0.409
AIP	-0.1581 ± 0.1373	0.1029 ± 0.0482	0.2631 ± 0.0504	0.5272 ± 0.1773	< 0.001
AIP indicates atherogenic index of plasma; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; PP, pulse pressure; CHD, coronary heart disease; MI, myocardial infarction; PCI, percutaneous coronary intervention; TIA, transient ischemic attack; PAD, peripheral artery disease; LVEF, left ventricular ejection fraction; UAP, unstable angina pectoris; NSTEMI, non ST-segment elevation myocardial infarction; STEMI, ST-segment elevation myocardial infarction; BUN, blood urea nitrogen; SCr, serum creatinine; UA, uric acid; TC, total cholesterol; LDL-C, low-density lipoprotein-cholesterol; HDL-C, high-density lipoprotein-cholesterol; FPG, fasting plasma glucose.

Table 2

Use of medications, agiographic findings, and procedural results of the study population according to the AIP quartiles
Variable	Q1 n = 199	Q2 n = 200	Q3 n = 200	Q4 n = 199	P value
Medications before admission
Aspirin, n (%)	145 (72.9)	149 (74.5)	154 (77.0)	146 (73.4)	0.784
P2Y12 inhibitors, n (%)	81 (40.7)	81 (40.5)	74 (37.0)	80 (40.2)	0.858
Statins, n (%)	150 (75.4)	143 (71.5)	155 (77.5)	142 (71.4)	0.418
ACEIs/ARBs, n (%)	55 (27.6)	65 (32.5)	72 (36.0)	69 (34.7)	0.300
β-blockers, n (%)	70 (35.2)	79 (39.5)	93 (46.5)	63 (31.7)	0.016
Insulin, n (%)	75 (37.7)	73 (36.5)	79 (39.5)	63 (31.7)	0.406
Oral antidiabetic agents, n (%)	89 (44.7)	98 (49.0)	110 (55.0)	90 (45.2)	0.145
Intraoperative anticoagulants
Unfractionated heparin, n (%)	168 (84.4)	170 (85.0)	154 (77.0)	156 (78.4)	0.086
LMWH, n (%)	6 (3.0)	7 (3.5)	16 (8.0)	10 (5.0)	0.089
Bivalirudin, n (%)	25 (12.6)	23 (11.5)	30 (15.0)	33 (16.6)	0.448
Perioperative medications
Aspirin, n (%)	196 (98.5)	198 (99.0)	200 (100.0)	196 (98.5)	0.308
P2Y12 inhibitors, n (%)	199 (100.0)	200 (100.0)	200 (100.0)	199 (100.0)	-
GP Ⅱb/Ⅲa receptor antagonist, n (%)	29 (14.6)	38 (19.0)	45 (22.5)	37 (18.6)	0.246
Medications at discharge
Aspirin, n (%)	196 (98.5)	198 (99.0)	200 (100.0)	196 (98.5)	0.308
Cilostazol, n (%)	3 (1.5)	2 (1.0)	1 (0.5)	4 (2.0)	0.473
Clopidogrel, n (%)	174 (87.4)	183 (91.5)	178 (89.0)	190 (95.5)	0.031
Ticagrelor, n (%)	25 (12.6)	17 (8.5)	22 (11.0)	9 (4.5)	0.031
Statins, n (%)	199 (100.0)	200 (100.0)	200 (100.0)	199 (100.0)	-
ACEIs/ARBs, n (%)	81 (40.7)	93 (46.5)	105 (52.5)	121 (60.8)	0.001
β-blockers, n (%)	138 (69.3)	147 (73.5)	155 (77.5)	136 (68.3)	0.155
Insulin, n (%)	65 (32.7)	76 (38.0)	72 (36.0)	57 (28.6)	0.213
Oral antidiabetic agents, n (%)	87 (43.7)	120 (60.0)	119 (59.5)	105 (52.8)	0.003
Angiographic findings
One-vessel disease, n (%)	22 (11.1)	15 (7.5)	18 (9.0)	25 (12.6)	0.347
Two-vessel disease, n (%)	68 (34.2)	41 (20.5)	42 (21.0)	46 (23.1)	0.004
LM/three-vessel disease, n (%)	109 (54.8)	144 (72.0)	140 (70.0)	128 (64.3)	0.001
Proximal LAD stenosis, n (%)	94(47.2)	110 (55.0)	97 (48.5)	100 (50.3)	0.428
Restenotic lesions, n (%)	26 (13.1)	26 (13.0)	30 (15.0)	29 (14.6)	0.913
Trifurcation or bifurcation lesions, n (%)	155 (77.9)	162 (81.0)	151 (75.5)	148 (74.4)	0.403
Chronic total occlusions, n (%)	36 (18.1)	46 (23.0)	44 (22.0)	57 (28.6)	0.093
Thrombus lesions, n (%)	7 (3.5)	14 (7.0)	12 (6.0)	12 (6.0)	0.478
Heavy calcification lesions, n (%)	75 (37.7)	69 (34.5)	73 (36.5)	48 (24.1)	0.016
Lesions > 20 mm long, n (%)	108 (54.3)	112 (56.0)	122 (61.0)	117 (58.8)	0.537
Procedural results
Target vessel territory
LM, n (%)	13 (6.5)	16 (8.0)	9 (4.5)	18 (9.0)	0.313
LAD, n (%)	104 (52.3)	97 (48.5)	100 (50.0)	99 (49.7)	0.899
LCX, n (%)	64 (32.2)	69 (34.5)	42 (21.0)	48 (24.1)	0.007
RCA, n (%)	69 (34.7)	82 (41.0)	100 (50.0)	74 (37.2)	0.011
DES use, n (%)	174 (87.4)	172 (86.0)	161 (80.5)	159 (79.9)	0.097
BRS use, n (%)	6 (3.0)	7 (3.5)	8 (4.0)	11 (5.5)	0.607
DCB use, n (%)	12 (6.0)	11 (5.5)	19 (9.5)	14 (7.0)	0.409
Complete revascularization, n (%)	136 (68.3)	115 (57.5)	110 (55.0)	107 (53.8)	0.012
AIP indicates atherogenic index of plasma; ACEIs, angiotensin converting enzyme inhibitors; ARBs, angiotensin II receptor blockers; LM, left-main artery; LAD, left anterior descending artery; LCX, left circumflex artery; RCA; right coronary artery; DES, drug-eluting stent; BRS, bioresorbable scaffold; DCB, drug-coated balloon.

Patients were followed up for a median of 927 days (interquartile range, 774 to 1109 days). During the period 198 patients developed at least one adverse CV event which was found in 33 (16.6%) patients from the Q1 group, 45 (22.5%) from the Q2 group, 54 (27.0%) from the Q3 group, and 66 (33.2%) from the Q4 group. Of the 198 patients with at least one adverse CV event, 20 died (18 died from CV causes, and 2 died from non-CV causes), 17 developed non-fatal ischemic stroke, 24 developed non-fatal MI, and 180 developed unplanned repeat revascularization; among these patients, 33 had two, 2 had three, and 2 had four adverse CV events. The baseline clinical and laboratory characteristics of the study population stratified by the primary endpoint are shown in Table 3. Patients who had at least one event had higher levels of AIP than those who had no events. Patients with at least one event had higher rates of previous MI, past PCI, peripheral arterial disease (PAD), and cardiac failure, higher levels of PP, SCr, triglycerides, and FPG, but lower levels of diastolic blood pressure, HDL-C, and LVEF. Use of medications, agiographic findings, and procedural results of the study population stratified by the primary endpoint are presented in Table 4. With the exception of ACEIs/ARBs, there was no difference in use of medications before admission between patients with and without events. With the exception of aspirin, use of perioperative medications was similar between patients with and without events. With the exception of aspirin, cilostazol, and insulin, patients with and without events received similar medications at discharge. Patients who had at least one event had higher proportions of left main/three-vessel disease, lesions > 20 mm long, and restenotic lesions, but lower proportions of one-vessel and two-vessel diseases, compared with those who had no events. In terms of procedural results, patients who had at least one event were more likely to use drug-coated balloons, while less likely to achieve complete revascularization.

Table 3

Baseline clinical and laboratory characteristics of the study population stratified by the primary endpoint
Variable	No such events n = 600	Primary endpoint n = 198	P value
Demographics
Age (years)	61 ± 10	62 ± 10	0.435
Male sex, n (%)	436 (72.7)	144 (72.7)	0.987
Clinical values (on admission)
BMI (kg/m²)	26.1 ± 3.2	25.6 ± 3.1	0.087
SBP (mm Hg)	131 ± 17	133 ± 16	0.399
DBP (mm Hg)	77 ± 10	72 ± 10	< 0.001
PP (mm Hg)	55 ± 15	60 ± 15	< 0.001
Risk factors
Smoking status
Current smoking, n (%)	244 (40.7)	80 (40.4)	0.948
Former smoking, n (%)	93 (15.5)	37 (18.7)	0.292
Never smoking, n (%)	263 (43.8)	81 (40.9)	0.471
Chronically daily drinking, n (%)	61 (10.2)	18 (9.1)	0.660
Family history of CHD, n (%)	173 (28.8)	66 (33.3)	0.231
Hypertension, n (%)	408 (68.0)	138 (69.7)	0.656
Dyslipidaemia, n (%)	496 (82.7)	172 (86.9)	0.165
Previous MI, n (%)	114 (19.0)	56 (28.3)	0.006
Past PCI, n (%)	123 (20.5)	68 (34.3)	< 0.001
Previous ischemic stroke or TIA, n (%)	39 (6.5)	12 (6.1)	0.827
PAD, n (%)	66 (11.0)	56 (28.3)	< 0.001
Cardiac failure, n (%)	39 (6.5)	28 (14.1)	0.001
LVEF (%)	65 (60–68)	63 (57–67)	0.002
Clinical presentation UAP, n (%)	467 (77.8)	160 (80.8)	0.376
NSTEMI, n (%)	82 (13.7)	18 (9.1)	0.092
STEMI, n (%)	51 (8.5)	20 (10.1)	0.493
Laboratory measurements (fasting state)
BUN (mmol/L)	5.4 (4.5–6.3)	5.6 (4.5–6.7)	0.179
SCr (µmol/L)	68.5 (60.9–78.5)	70.7 (63.1–83.5)	0.010
UA (µmol/L)	333.2 ± 77.2	344.4 ± 78.9	0.079
TC (mmol/L)	4.10 ± 1.03	4.24 ± 0.98	0.093
LDL-C (mmol/L)	2.41 ± 0.83	2.47 ± 0.74	0.382
HDL-C (mmol/L)	1.04 ± 0.22	0.96 ± 0.20	< 0.001
Triglycerides (mmol/L)	1.46 (1.04-2.00)	1.67 (1.12–2.31)	0.003
FPG (mmol/L)	6.88 (6.12–8.01)	7.89 (6.70–9.23)	< 0.001
Glycated haemoglobin (%)	7.2 (6.6–8.1)	7.4 (6.8–8.3)	0.079
AIP	0.1608 ± 0.2653	0.2535 ± 0.2919	< 0.001
AIP			0.001
Q1, n (%)	166 (27.7)	33 (16.7)	-
Q2, n (%)	155 (25.8)	45 (22.7)	-
Q3, n (%)	146 (24.3)	54 (27.3)	-
Q4, n (%)	133 (22.2)	66 (33.3)	-
Abbreviations as in Tables 1 and 2.

Table 4

Use of medications, agiographic findings, and procedural results of the study population stratified by the primary endpoint
Variable	No such events n = 600	Primary endpoint n = 198	P value
Medications before admission
Aspirin, n (%)	442 (73.7)	152 (76.8)	0.386
P2Y12 inhibitors, n (%)	234 (39.0)	82 (41.4)	0.547
Statins, n (%)	438 (73.0)	152 (76.8)	0.295
ACEIs/ARBs, n (%)	183 (30.5)	78 (39.4)	0.021
β-blockers, n (%)	233 (38.8)	72 (36.4)	0.535
Insulin, n (%)	212 (35.3)	78 (39.4)	0.303
Oral antidiabetic agents, n (%)	289 (48.2)	98 (49.5)	0.746
Intraoperative anticoagulants
Unfractionated heparin, n (%)	486 (81.0)	162 (81.8)	0.798
LMWH, n (%)	29 (4.8)	10 (5.1)	0.902
Bivalirudin, n (%)	85 (14.2)	26 (13.1)	0.715
Perioperative medications
Aspirin, n (%)	600 (100.0)	190 (96.0)	< 0.001
P2Y12 inhibitors, n (%)	600 (100.0)	198 (100.0)	-
GP Ⅱb/Ⅲa receptor antagonist, n (%)	105 (17.5)	44 (22.2)	0.139
Medications at discharge
Aspirin, n (%)	600 (100.0)	190 (96.0)	< 0.001
Cilostazol, n (%)	2 (0.3)	8 (4.0)	< 0.001
Clopidogrel, n (%)	545 (90.8)	180 (90.9)	0.974
Ticagrelor, n (%)	55 (9.2)	18 (9.1)	0.974
Statins, n (%)	600 (100.0)	198 (100.0)	-
ACEIs/ARBs, n (%)	294 (49.0)	106 (53.5)	0.268
β-blockers, n (%)	442 (73.7)	134 (67.7)	0.103
Insulin, n (%)	190 (31.7)	80 (40.4)	0.024
Oral antidiabetic agents, n (%)	324 (54.0)	106 (53.5)	0.909
Angiographic findings
One-vessel disease, n (%)	72 (12.0)	8 (4.0)	0.001
Two-vessel disease, n (%)	165 (27.5)	32 (16.2)	0.001
LM/three-vessel disease, n (%)	363 (60.5)	158 (79.8)	< 0.001
Proximal LAD stenosis, n (%)	295 (49.2)	106 (53.5)	0.286
Restenotic lesions, n (%)	62 (10.3)	49 (24.7)	< 0.001
Trifurcation or bifurcation lesions, n (%)	460 (76.7)	156 (78.8)	0.537
Chronic total occlusions, n (%)	141 (23.5)	42 (21.2)	0.507
Thrombus lesions, n (%)	38 (6.3)	7 (3.5)	0.139
Heavy calcification lesions, n (%)	197 (32.8)	68 (34.3)	0.696
Lesions > 20 mm long, n (%)	319 (53.2)	140 (70.7)	< 0.001
Procedural results
Target vessel territory
LM, n (%)	40 (6.7)	16 (8.1)	0.499
LAD, n (%)	302 (50.3)	98 (49.5)	0.838
LCX, n (%)	168 (28.6)	52 (27.7)	0.808
RCA, n (%)	243 (40.5)	82 (41.4)	0.820
DES use, n (%)	502 (83.7)	164 (82.8)	0.783
BRS use, n (%)	27 (4.5)	5 (2.5)	0.219
DCB use, n (%)	34 (5.7)	22 (11.1)	0.009
Complete revascularization, n (%)	384 (64.0)	84 (42.4)	< 0.001
Abbreviations as in Tables 1 and 2.

Kaplan-Meier analyses revealed a significantly higher incidence of the primary endpoint (log-rank test, P = 0.001; Fig. 1a) and a marginally (but non-significantly) higher incidence of the key secondary endpoint (log-rank test, P = 0.114; Fig. 1b) in patients with higher AIP values. The difference in the incidence of the primary endpoint was mainly due to an increase in unplanned repeat revascularization (log-rank test, P = 0.005; Fig. 1f) across the AIP quartiles. However, the incidence of all-cause death (log-rank test, P = 0.168; Fig. 1c), CV death (log-rank test, P = 0.459), non-fatal ischemic stroke (log-rank test, P = 0.167; Fig. 1d), and non-fatal MI (log-rank test, P = 0.636; Fig. 1e) did not differ among the AIP quartiles at follow-up.

Table 5 shows univariate and multivariate Cox proportional hazards analyses for the primary endpoint at follow up, which includes AIP quartiles, age, BMI, PP, LVEF, SCr, LDL-C, FPG, glycosylated haemoglobin, sex, current smoking, hypertension, previous MI, past PCI, PAD, cardiac failure, clinical presentation, coronary artery disease (CAD) severity, lesions > 20 mm long, restenotic lesions, use of drug-coated balloon, complete revascularization, and use of insulin at discharge. A multivariate Cox proportional hazards analysis, with Q1 as a reference, showed that the AIP for Q2, Q3, and Q4 had increased HRs for the incidence of the primary endpoint (Q2: HR 1.164, 95% CI 0.728–1.860; Q3: HR 1.640, 95% CI 1.032–2.606; Q4: HR 2.153, 95% CI 1.355–3.421). Moreover, AIP used as a continuous variable was independently predictive of the primary endpoint (HR, 2.581; 95% CI, 1.499–4.444; P = 0.001). Table 6 shows univariate and multivariate Cox proportional hazards analyses for the key secondary endpoint at follow up, which includes AIP quartiles, age, BMI, PP, LVEF, blood urea nitrogen, SCr, LDL-C, FPG, glycosylated haemoglobin, current smoking, hypertension, past PCI, PAD, cardiac failure, clinical presentation, CAD severity, lesions > 20 mm long, complete revascularization, and use of ACEIs/ARBs at discharge. A multivariate Cox proportional hazards analysis, with Q1 as a reference, showed that the AIP for Q2, Q3, and Q4 had increased HRs for the incidence of the key secondary endpoint (Q2: HR 1.063, 95% CI 0.397–2.849; Q3: HR 1.762, 95% CI 0.690–4.497; Q4: HR 2.613, 95% CI 1.024–6.666). Moreover, AIP used as a continuous variable was independently predictive of the key secondary endpoint (HR, 3.412; 95% CI, 1.086–10.723; P = 0.036).

Table 5

Relationship between the incidence of the primary endpoint and the AIP expressed as a categorical variable
Variables	Univariate analysis HR (95% CI)	P-value	Multivariate analysis HR (95% CI)	P-value
AIP quartiles
Q1	Reference		Reference
Q2	1.376 (0.878–2.156)	0.164	1.164 (0.728–1.860)	0.525
Q3	1.710 (1.109–2.636)	0.015	1.640 (1.032–2.606)	0.036
Q4	2.265 (1.491–3.440)	< 0.001	2.153 (1.355–3.421)	0.001
Age	1.005 (0.991–1.019)	0.472	0.989 (0.972–1.007)	0.219
BMI	0.959 (0.916–1.005)	0.078	0.931 (0.884–0.982)	0.008
PP	1.021 (1.012–1.029)	< 0.001	1.019 (1.008–1.029)	< 0.001
LVEF	0.974 (0.959–0.990)	0.002	0.987 (0.965–1.009)	0.242
SCr	1.013 (1.006–1.021)	0.001	1.011 (1.003–1.020)	0.010
LDL-C	1.078 (0.916–1.269)	0.366	1.021 (0.842–1.239)	0.832
FPG	1.201 (1.132–1.274)	< 0.001	1.192 (1.108–1.281)	< 0.001
Glycosylated haemoglobin	1.067 (0.955–1.193)	0.252	0.861 (0.739–1.003)	0.055
Male sex	0.966 (0.706–1.320)	0.827	0.834 (0.549–1.266)	0.394
Current smoking	1.003 (0.755–1.332)	0.983	1.256 (0.874–1.805)	0.219
Hypertension	1.068 (0.789–1.447)	0.669	1.040 (0.732–1.478)	0.825
Previous MI	1.492 (1.095–2.033)	0.011	0.812 (0.549–1.202)	0.299
Past PCI	1.723 (1.285–2.310)	< 0.001	1.157 (0.687–1.949)	0.583
PAD	2.511 (1.836–3.435)	< 0.001	1.601 (1.096–2.338)	0.015
Cardiac failure	1.951 (1.308–2.909)	0.001	1.167 (0.661–2.062)	0.594
Clinical presentation
UAP	Reference		Reference
NSTEMI	0.677 (0.416–1.102)	0.117	0.531 (0.310–0.909)	0.021
STEMI	1.098 (0.690–1.747)	0.695	1.114 (0.649–1.913)	0.695
CAD severity
One-vessel disease	Reference		Reference
Two-vessel disease	1.756 (0.809–3.810)	0.154	1.324 (0.589–2.975)	0.497
LM/three-vessel disease	3.541 (1.740–7.205)	< 0.001	1.876 (0.882–3.990)	0.103
Restenotic lesions	2.290 (1.658–3.163)	< 0.001	1.724 (0.947–3.138)	0.075
Lesions > 20 mm long	1.984 (1.461–2.695)	< 0.001	1.574 (1.132–2.189)	0.007
DCB use	1.913 (1.228–2.980)	0.004	1.025 (0.589–1.784)	0.931
Complete revascularization	0.467 (0.352–0.619)	< 0.001	0.736 (0.538–1.008)	0.056
Insulin at discharge	1.396 (1.051–1.854)	0.021	1.135 (0.818–1.574)	0.448
Abbreviations as in Tables 1 and 2.

Table 6

Relationship between the incidence of the key secondary endpoint and the AIP expressed as a categorical variable
Variables	Univariate analysis HR (95% CI)	P-value	Multivariate analysis HR (95% CI)	P-value
AIP quartiles
Q1	Reference		Reference
Q2	1.380 (0.555–3.432)	0.488	1.063 (0.397–2.849)	0.903
Q3	2.020 (0.865–4.720)	0.104	1.762 (0.690–4.497)	0.236
Q4	2.477 (1.085–5.659)	0.031	2.613 (1.024–6.666)	0.044
Age	1.042 (1.013–1.072)	0.004	1.017 (0.982–1.053)	0.343
BMI	0.915 (0.834–1.004)	0.062	0.844 (0.751–0.948)	0.004
PP	1.030 (1.014–1.046)	< 0.001	1.012 (0.991–1.034)	0.265
LVEF	0.924 (0.902–0.946)	< 0.001	0.945 (0.906–0.984)	0.007
BUN	1.166 (1.018–1.335)	0.026	0.868 (0.724–1.041)	0.127
SCr	1.025 (1.014–1.036)	< 0.001	1.016 (1.001–1.030)	0.031
LDL-C	1.064 (0.774–1.463)	0.702	1.081 (0.714–1.636)	0.713
FPG	1.097 (0.967–1.243)	0.150	1.017 (0.880–1.176)	0.818
Glycated haemoglobin	1.090 (0.882–1.347)	0.425	1.220 (0.932–1.597)	0.148
Current smoking	1.014 (0.589–1.746)	0.959	1.534 (0.824–2.855)	0.177
Hypertension	1.333 (0.725–2.450)	0.354	1.266 (0.608–2.636)	0.528
Past PCI	1.904 (1.096–3.309)	0.022	2.247 (1.170–4.313)	0.015
PAD	5.804 (3.402–9.902)	< 0.001	2.888 (1.545–5.399)	0.001
Cardiac failure	6.939 (3.993–12.061)	< 0.001	1.888 (0.767–4.648)	0.167
Clinical presentation
UAP	Reference		Reference
NSTEMI	1.333 (0.622–2.857)	0.460	0.501 (0.199–1.258)	0.141
STEMI	1.920 (0.896–4.115)	0.094	1.056 (0.427–2.610)	0.906
CAD severity
One-vessel disease	Reference		Reference
Two-vessel disease	0.201 (0.037–1.096)	0.064	0.059 (0.009–0.402)	0.004
LM/three-vessel disease	1.910 (0.689–5.296)	0.214	0.418 (0.121–1.444)	0.168
Lesions > 20 mm long	2.681 (1.411–5.029)	0.003	1.828 (0.882–3.789)	0.105
Complete revascularization	0.403 (0.232-0.700)	0.001	0.684 (0.348–1.343)	0.270
ACEIs/ARBs at discharge	2.933 (1.596–5.391)	0.001	1.782 (0.883–3.599)	0.107
Abbreviations as in Tables 1 and 2.

From the results of the multivariate Cox proportional hazards analysis, we calculated the Harrell’s C statistic for the primary endpoint. The Harrell’s C statistic of the variables, including BMI, PP, SCr, FPG, PAD, clinical presentation, and lesions > 20 mm long, was 0.697 versus 0.707 after the addition of AIP quartiles; the continuous NRI was 19.1% (7.9–35.6%; P < 0.001).

The predictive impacts of AIP as a continuous variable for the primary endpoint were further assessed in different subgroups of the study population. Increased AIP value (per 1-unit) was consistently associated with the primary endpoint in different subgroups, including age < 60 versus ≥ 60 years, BMI < 28 versus ≥ 28 kg/m², LDL-C ≤ 1.8 versus > 1.8 mmol/L, female versus male, with versus without hypertension, and non-ST versus ST segment elevation ACS (Fig. 2).

The main findings of this study were as follows: (1) patients with higher AIP values had a significantly higher probability of adverse CV events by the log-rank test; (2) multivariate Cox proportional hazards analyses showed that AIP was independently and strongly associated with the primary and key secondary endpoints in T2DM and ACS patients who underwent PCI, suggesting that AIP might have a potential role in early risk stratification of such patients; (3) a significant continuous NRI showed that predictive model was improved after adding AIP to the model of other independent predictors. To the best of our knowledge, this is the first report to describe the prognostic impact of AIP in T2DM patients with ACS undergoing PCI.

Dyslipidemia plays a crucial role in the pathogenesis and progression of coronary atherosclerosis. Diabetic patients are more likely to develop coronary atherosclerosis than non-diabetic patients, which may be significantly associated with so-called diabetic dyslipidemia, which consists of elevated plasma concentrations of triglyceride-rich lipoproteins, small dense LDL-C particles and low levels of HDL-C. The different components of diabetic dyslipidemia are not isolated abnormalities but closely linked to each other metabolically [9]. Triglycerides and HDL-C are two lipid parameters measured routinely in clinical practice; however, neither is a consistently good proxy for plasma atherogenicity. AIP, a novel lipid index defined as log₁₀ (triglycerides/HDL-C), has been demonstrated to be closely correlated with FER(HDL) and lipoprotein particle size both of which are directly involved in the pathogenesis and development of atherosclerosis, and thus AIP is regarded as an excellent indicator of atherosclerosis and can offer a benefit to identify the risk of CV disease [31].

Multiple cross-sectional studies reported that AIP was a strong predictor of CAD independent of diabetes [23, 24, 26]. Notably, the Indian Atherosclerosis Research Study revealed that addition of AIP and family history to traditional risk factors improved risk discrimination (C-index: from 0.864 to 0.873) in Asian Indians with CAD [19]. Intriguingly, Frohlich J, et al. found that AIP was an independent predictor of angiographically defined CAD only when FER(HDL) was omitted from multivariate analysis, which may be due to a clear internal correlation between AIP and FER(HDL) [17]. Moreover, Nam JS, et al. found that there is a significant correlation between AIP and the progression of coronary artery calcification measured by using a multi-detector computed tomography in subjects without cardiovascular disease [28]. Furthermore, in a prospective cohort study including 2676 middle-aged adults followed for 7.8 years, researchers demonstrated that the top quartiles of AIP predicted significantly age-adjusted incident CAD in both sexes, more strongly in women, after adjustment for C-reactive protein and traditional risk factors [18].

AIP has been demonstrated to be associated with mortality in elder patients and dialysis patients. Edwards MK, et al. analyzed data from the 1999–2006 National Health and Nutrition Examination Survey with follow-up through 2011 to find that AIP was positively and independently associated with mortality risk and predicted mortality risk better than individual cholesterol risk factors, among an older adult population [20]. Bendzala M, et al. also found that AIP was positively associated with the risk of all-cause death in elderly women with hypertension [21]. However, a Korean nationwide prospective cohort study including 1174 incident dialysis patients showed that AIP had a non-linear relationship with survival; both the highest and the lowest AIP quintiles were independently associated with all-cause mortality, showing a U-shaped association [22].

The predictive value of AIP has also been explored in ACS patients. Cai G, et al. retrospectively enrolled 1478 very young participants (≤ 35 years of age) undergoing coronary angiography and divided them into two groups: ACS group (n = 1058) and non-CAD group (n = 419). They found that AIP was independently associated with the presence and severity of ACS in a gender-dependent manner and the prevalence of ACS, acute MI, unstable angina pectoris and the value of Gensini Score (a scoring system for evaluating CAD severity) were elevated as AIP quartiles increased [25]. Qin Z, et al. retrospectively enrolled 2356 T2DM patients who underwent PCI and followed them for 4 years. They found that AIP was an independent predictor of major cardiovascular and cerebrovascular adverse events including cardiac death, MI, repeated revascularization, and stroke, regardless of clinical presentation [27].

AIP was reported to be positively correlated with serum malondialdehyde in menopausal women with CV disease [32]. And we know malondialdehyde can reflect the status of oxidative stress which is significantly associated with coronary atherosclerosis [33]. AIP was found to be associated with epicardial adipose tissue measured by using a transthoracic echocardiography or an electrocardiogram-gated multidetector computed tomography [34, 35]. Evidence indicates that epicardial adipose tissue directly affects coronary atherosclerosis [36]. AIP was shown to be directly and independently associated with arterial stiffness in normotensive and never-treated hypertensive subjects [37]. Increased aortic stiffness often results in early wave reflection of the aortic pulse wave which increases systolic blood pressure but decreases diastolic blood pressure, and these hemodynamic changes impair coronary perfusion, which can promote adverse CV events [30]. AIP was demonstrated to be a marker for reduced coronary flow reserve [38], and the latter was positively associated with adverse CV events. Of note, AIP includes triglycerides and HDL-C in its formula. High triglycerides and low HDL-C levels were shown to be associated with adverse CV events after ACS, independent of diabetic status [39, 40].

Glucose-lowering regiments including pioglitazone have been shown to be effective in reducing AIP values, thereby reducing CV risk [41, 42]. Moreover, moderate-to-vigorous physical activity, increasing aerobic exercise time, decreased sedentary behavior, and consequent high levels of cardiorespiratory fitness were reported to be inversely correlated with AIP, which implies that a healthy lifestyle helps attenuate the risk for CV disease via improvements in AIP [43–47]. As a result, use of pioglitazone and early initiation of physical activity and aerobic exercise may be important to reduce future CV risk in T2DM patients with ACS undergoing PCI who have received first-line medication therapy but still have significantly increased values of AIP.

Our study also had several important limitations. First, this was a retrospective analysis of a single centre prospective registry, which could not definitively establish causality. Second, the baseline concentrations of triglycerides and HDL-C might be affected by the use of statins before admission and glycemic control status. However, there were no significant differences among the four groups on the basis of the AIP quartiles with respect to the use of statins before admission and glycated haemoglobin which can consistently reflect glycemic control status in diabetic patients. Third, follow-up data were obtained merely via telephone; however, we confirm the authenticity of adverse events by reviewing corresponding medical records. Finally, whether the findings from this study including only Chinese patients can be extrapolated to other ethnic groups will require further studies.

A higher AIP value on admission was independently and strongly associated with adverse CV events in T2DM patients with ACS undergoing PCI, suggesting that AIP might have a potential role in early risk stratification of such patients. Medical management optimization according to AIP could result in a reduced risk of subsequent CV events.

ACEIs/ARBs

angiotensin converting enzyme inhibitors/angiotensin II receptor blockers; ACS:acute coronary syndrome; AIP:atherogenic index of plasma; BMI:body mass index; CABG:coronary artery bypass grafting; CAD:coronary artery disease; CI:confidence interval; CV:cardiovascular; FER(HDL):cholesterol esterification rates in apoB-lipoprotein-depleted plasma; FPG:fasting plasma glucose; HDL-C:high-density lipoprotein cholesterol; HR:hazard ratio; LDL-C:low-density lipoprotein cholesterol; LVEF:left ventricular ejection fraction; MI:myocardial infarction; NRI:net reclassification improvement; PAD:peripheral artery disease; PCI:percutaneous coronary intervention; PP:pulse pressure; SCr:serum creatinine; T2DM:type 2 diabetes mellitus; TC:total cholesterol.

This article is present on a university repository website and can be accessed on https://www.researchsquare.com/article/rs-67794/v1. This article is not published nor is under publication elsewhere.

Ethics approval and consent to participate

This study was approved by the institutional review board of Beijing Anzhen Hospital, Capital Medical University. Given the retrospective nature of this study, the requirement for informed consent was waived.

Consent for publication

Not applicable.

Availability of data and materials

The data, analytic methods, and study materials will not be made available to other researchers for purposes of reproducing the results or replicating the procedure.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by the grant from National Key Research and Development Program of China (2017YFC0908800), Beijing Municipal Administration of Hospitals’ Ascent Plan (DFL20150601) and Mission Plan (SML20180601), Beijing Municipal Health Commission - “Project of Science and Technology Innovation Center” (PXM2019_026272_000006) (PXM2019_026272_000005), and National Natural Science Foundation of China (81670391).

Authors’ contributions

Xiaoteng Ma, Yan Sun, and Yujing Cheng contributed equally to this paper. All authors were involved in the conception and design of the study and in the collection, analysis, and interpretation of the data. All authors reviewed the final manuscript. All authors read and approved the final manuscript.

Acknowledgements

Not applicable.

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Prognostic Impact of Atherogenic Index of Plasma (AIP) In Type 2 Diabetes Mellitus Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention

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