Journal of Atherosclerosis and Thrombosis
Online ISSN : 1880-3873
Print ISSN : 1340-3478
ISSN-L : 1340-3478
Original Article
Association between Non-Lipid Residual Risk Factors and Cardiovascular Events in Patients with Stable Coronary Artery Disease Treated with Pitavastatin: An Observation from the REAL-CAD Study
Kiwamu Kamiya, Makoto Takei, Toshiyuki Nagai, Toru Miyoshi, Hiroshi Ito, Yoshihiro Fukumoto, Hitoshi Obara, Tatsuyuki Kakuma, Ichiro Sakuma, Hiroyuki Daida, Satoshi Iimuro, Hiroaki Shimokawa, Takeshi Kimura, Ryozo Nagai, Toshihisa Anzai
著者情報
キーワード: Stable coronary artery disease, Statin, Residual risk
ジャーナル オープンアクセス HTML

2024 年 31 巻 1 号 p. 61-80

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Abstract

Aims: We aimed to investigate the association between non-lipid residual risk factors and cardiovascular events in patients with stable coronary artery disease (CAD) who achieved low-density lipoprotein cholesterol (LDL-C) ＜100 mg/dL from the Randomized Evaluation of Aggressive or Moderate Lipid Lowering Therapy with Pitavastatin in Coronary Artery Disease (REAL-CAD) study.

Methods: The REAL-CAD study was a prospective, multicenter, open-label trial. As a sub-study, we examined the prognostic impact of non-lipid residual risk factors, including blood pressure, glucose level, and renal function, in patients who achieved LDL-C ＜100 mg/dL at 6 months after pitavastatin therapy. Each risk factor was classified according to severity. The primary outcome was a composite of cardiovascular death, nonfatal myocardial infarction, nonfatal ischemic stroke, and unstable angina requiring emergency hospitalization.

Results: Among 8,743 patients, the mean age was 68±8.2 years, and the mean LDL-C level was 84.4±18 mg/dL. After adjusting for the effects of confounders, an estimated glomerular filtration rate (eGFR) ≤ 60 mL/min/1.73 m2 showed the highest risk of the primary outcome (hazard ratio [HR] 1.92; 95% confidence interval [CI] 1.45-2.53). The combination of eGFR ≤ 60 and hemoglobin A1c (HbA1c) ≥ 6.0% also showed the highest risk of all-cause death (HR, 2.42; 95% CI, 1.72-3.41).

Conclusions: In patients with stable CAD treated with pitavastatin and who achieved guidelines-directed levels of LDL-C, eGFR and HbA1c were independently associated with adverse events, suggesting that renal function and glycemic control could be residual non-lipid therapeutic targets after statin therapy.

See editorial vol. 31: 21-22

Introduction

Statins are common and effective in reducing low-density lipoprotein cholesterol (LDL-C) levels and the risk of major cardiovascular (CV) events, such as cardiac death, nonfatal myocardial infarction, and nonfatal ischemic stroke in primary and secondary prevention1, 2). In particular, current guidelines recommend LDL-C lowering strategies with high-intensity statins in patients with coronary artery disease (CAD) and those at high risk of atherosclerotic cardiovascular disease (ASCVD)3-5). On the other hand, recent clinical trials have shown that statins only prevent approximately 30% of CV events, while the remaining 70% of CV events persist1, 6-8). The importance of controlling these residual risks has recently attracted particular attention and is being examined from various perspectives.

Although patients with existing ASCVD should be managed with the goal of achieving LDL-C ＜100 mg/dL3, 4), coronary artery atherosclerosis is also accelerated by the accumulation of non-lipid risk factors such as hypertension, diabetes, and chronic kidney disease (CKD)9). In particular, hypertension and diabetes have been implicated in developing atherosclerotic diseases, including endothelial dysfunction, vascular inflammation, and arterial remodeling through up-regulation of the renin-angiotensin-aldosterone system, oxidative stress and inflammation, and activation of the immune system10). Furthermore, CKD is not only an independent risk factor for developing ASCVD, but also associated with adverse outcomes in those with existing ASCVD11-13). Despite advances in high intensity statin therapy, non-lipid risk factors would remain independent predictors of cardiovascular mortality in patients with CAD. In the Treat to New Targets (TNT) trial14), the non-lipid residual risk factors for subsequent CV events were obese, hypertension, diabetes, and blood urea nitrogen levels in patients with clinically evident stable CAD who were treated with atorvastatin15). Furthermore, we previously reported that CKD was a non-lipid residual risk factor for subsequent CV events in patients with stable CAD treated with high-dose (4 mg/day) pitavastatin16). However, in these studies, not all patients could achieve the current guideline-directed target LDL-C levels (＜100 mg/dL). Therefore, there is a paucity of data investigating the prognostic impact of the non-lipid risk factors that could be present in patients with CAD, and achieved the control goal of LDL-C ＜100 mg/dL.

Aim

We sought to investigate the association between non-lipid residual risk factors and CV events in patients with stable CAD who achieved LDL-C ＜100 mg/dL from the Randomized Evaluation of Aggressive or Moderate Lipid Lowering Therapy with Pitavastatin in Coronary Artery Disease (REAL-CAD) 17) substudy.

Methods

Study Design and Population

The present study was a post hoc analysis of the REAL-CAD study (Clinical Trial Registration: http://www.clinicaltrials.gov. Unique identifier: NCT01042730). The REAL-CAD study has previously been described17), and is a prospective, multicenter, randomized, open-label, blinded end point, physician initiated, superiority trial to determine whether high-dose (4 mg/day) pitavastatin therapy is more effective in reducing CV events than low-dose (1 mg/day) pitavastatin therapy in Japanese patients with stable CAD. Stable CAD was defined as a history of acute coronary syndrome (ACS) or coronary revascularization in the last three months or a clinical diagnosis of CAD with angiographically confirmed coronary stenosis ≥ 75% according to the American Heart Association classification18). The enrolled patients received 1 mg/day of oral pitavastatin for a run-in period of at least one month. Patients were excluded if they had LDL-C ≥ 120 mg/dL during the run-in period, ACS or coronary revascularization within the previous 3 months, poor adherence to statin therapy, a primary endpoint event, or other adverse events during the run-in period that prevented the continuation of the study. At the end of the run-in period, eligible patients were randomized in a 1:1 ratio to receive high or low oral doses of pitavastatin. Approval was obtained from the Ethics Review Board of the Public Health Research Foundation and the Ethics Review Board, of all participating institutions. Written consent was obtained from all participants. This study was conducted in accordance with the principles of the Declaration of Helsinki.

Of 14,774 participants enrolled in this study, we excluded 1,720 patients due to patient withdrawal, missing consent information, or other reasons. Subsequently, 13,054 patients with LDL-C ＜120 mg/dL, who received pitavastatin 1 mg/day at any time during the run-in period, were randomized to pitavastatin 1 mg/day or 4 mg/day. Additionally, we excluded 641 patients due to patient withdrawal, missing consent information, or not meeting the eligibility criteria. Among the complete set of analysis of 12,413 patients in the main study, we excluded 2,767 patients who did not reach LDL-C ＜100 mg/dL during the initial six months after randomization due to missing data on the following: systolic blood pressure (SBP), 287 patients; hemoglobin A1c (HbA1c), 569 patients; and estimated glomerular filtration rate (eGFR), 47 patients. Ultimately, 8,743 patients were examined (Fig.1).

Fig.1. Flow of patients through study

LDL-C, low-density lipoprotein cholesterol; HbA1c, hemoglobin A1c; SBP, systolic blood pressure; eGFR, estimated glomerular filtration rate.

Classification of Comorbidities

Hypertension was classified based on three criteria: use of antihypertensive medication: SBP (1) ≤ 120 mmHg, (2) 120-129 mmHg, (3) 130-139 mmHg, (4) ≥ 140 mmHg, and pulse pressure (1) ≤ 40 mmHg, (2) 40-49 mmHg, and (3) ≥ 50 mmHg. Diabetes mellitus was classified based on two criteria: use of antidiabetic medicine and HbA1c (1) ≤ 6.0%, (2) 6.0-7.9%, and (3) ≥ 8.0%. CKD was classified as follows: eGFR (1) ＜30 mL/min/1.73 m2, (2) 30-60 mL/min/1.73 m2, and (3) ＞60 mL/min/1.73 m2.

Clinical Outcomes

The primary outcome of interest was a composite of CV death, nonfatal myocardial infarction, nonfatal ischemic stroke, or unstable angina requiring emergent admission within six months after randomization, consistent with the primary analysis of the trial17). The secondary outcome of interest was defined as a composite of the primary outcome event and/or clinically indicated coronary revascularization, excluding target lesion revascularization for lesions treated at a previous percutaneous coronary intervention. Furthermore, death from any cause, CV death, and cardiac death were defined as outcomes for analysis.

Statistical Analysis

Continuous variables were presented as mean and standard deviation (SD), and categorical variables were expressed as numbers and percentages. The following statistical analyses were performed to investigate the proposed working hypothesis by examining the relationship between the incidence of the major events defined above and the risk factors after adjusting for the effects of the regulator variables, as described below. Blood pressure, glucose level, and renal function were considered risk factors. The risk factors described in the classification of comorbidities were subdivided and used in sensitivity analyses. Pitavastatin dose (1 mg vs. 4 mg), age, sex, body mass index, LDL-C, high-density lipoprotein cholesterol (HDL-C), triglycerides, C-reactive protein and heart rate were considered regulatory variables. The five major events were analyzed separately using the following exploratory statistical methods. We performed statistical analyses for each group to identify the combination of comorbid factors that could predict a worse prognosis. As using antihypertensive and antidiabetic medications was related to event development, each subgroup was analyzed separately. To evaluate the effect of risk factors on the incidence of the event, the classification, and regression tree (CART) survival tree was used to construct a set of ‘layers’ consisting of an asymmetric combination of risk factors. The hazard ratio (HR) among the layers was examined after controlling for the effects of the regulatory variables. Furthermore, from a clinical point of view, sensitivity analyses were performed in some extracted layers of the model constructed from each analysis dataset. From the parameter estimates obtained from these statistical analyses, we evaluated the appropriate parameters of blood pressure, glucose level, and renal function and verified the working hypotheses related to the therapeutic strategy and improvement of poor prognosis. All tests were two-tailed, and a value of P＜.05 was considered statistically significant. All statistical analyses were conducted by a statistician (Hitoshi Obara and Tatsuyuki Kakuma) using StataMP 16 statistical software (StataCorp, College Station, TX, USA).

Results

Baseline Characteristics

Among the 8,743 patients in the present study, 1,410 were women and 7,333 were men; the mean age was 68.3±8.2 years. Mean levels of LDL-C, HDL-C, and triglycerides at baseline were 84.4±18.0 mg/dL, 50.3±12.5 mg/dL, and 144.6±91.4 mg/dL, respectively. The mean systolic and diastolic blood pressures were 127.5±16.2 mmHg and 72.9±10.9 mmHg, respectively. Of all patients, 5,331 (61%) had eGFR ＞60 mL/min/1.73m2, 3,264 (37%) had 30 ≤ eGFR ≤ 60 mL/min/1.73m2, and 148 (2%) had eGFR ＜30 mL/min/1.73m2. Regarding glycemic control, 5,793 (66%) patients had HbA1c ＜6.0%, 2,716 (31%) had 6.0 ≤ HbA1c ＜8.0, and 234 (3%) had HbA1c ≥ 8.0%. In general, 83% of the patients (n=7,292) received antihypertensive medication at baseline: 62% received angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers, 39% received β-blockers, and 44% received calcium channel blockers. Furthermore, 2,366 (27%) patients received anti-diabetic medication:7% received metformin, 11% received dipeptidyl peptidase-4 inhibitors, 12% received sulfonylureas and 2% received glucagon-like peptide 1 receptor agonists (Table 1).

Table 1.Baseline characteristics

Variable Entire cohort (N= 8,743)
Age, years 68.3±8.2
Men, n (%) 7,333 (84)
Weight, kg 65.3±11.2
Body mass index, kg/m2 24.6±3.4
Heart rate, beats/min 69.6±11.6
Currently smoking, n (%) 1,370 (16)
SBP, mmHg 127.5±16.2
＜120 mmHg 2,564 (29)
120 – 129 mmHg 2,307 (26)
130 – 139 mmHg 2,051 (23)
≥ 140 mmHg 1,821 (22)
DBP, mmHg 72.9±10.9
Pulse pressure, mmHg 54.6±13.1
＜40 mmHg 883 (10)
40 – 49 mmHg 2,207 (25)
≥ 50 mmHg 5,653 (65)
HbA1c, % 5.9±0.8
＜6.0% 5,793 (66)
6.0 – 8.0% 2,716 (31)
≥ 8.0% 234 (3)
eGFR, ml/min/1.73 m2 65.7±18.7
＜30 mL/min/1.73m2 148 (2)
30 - 60 mL/min/1.73m2 3,264 (37)
＞60 mL/min/1.73m2 5,331 (61)
Blood examinations
Total cholesterol, mg/dL 163.7±25.4
LDL-C, mg/dL 84.4±18.0
HDL-C, mg/dL 50.3±12.5
Triglycerides, mg/dL 144.6±91.4
High-sensitivity C-reactive protein, mg/L 0.51 (0.25 - 1.19)
Comorbidities, n (%)
Cerebral infarction 627 (7)
Atrial fibrillation 571 (7)
Peripheral artery disease 617 (7)
Malignancies 483 (6)
Chronic heart failure 454 (5)
Prior myocardial infarction 4544 (52)
Dose of pitavastatin, n (%)
1 mg 4,399 (50)
4 mg 4,344 (50)
Anti-HT medicine, n (%) 7,292 (83)
ACE inhibitors /ARBs 5,440 (62)
β-blockers 3,427 (39)
Calcium channel blockers 3,880 (44)
Diuretics 1,566 (18)
Anti-DM medicine, n (%) 2,366 (27)
Dipeptidyl peptidase-4 inhibitors 917 (11)
Glucagon-like peptide 1 receptor agonists 149 (2)
Metformin 638 (7)
Sodium-glucose cotransporter 2 inhibitors 3 (0)
Sulfonylureas 1,008 (12)
Thiazolidinediones 670 (8)

Continuous variables are presented as mean±standard deviation if normally distributed, and median (interquartile range) if not normally distributed

Categorical variables are presented as number of patients (%).

Abbreviations: ACE, angiotensin converting enzyme; ARB, angiotensin II receptor blocker; DBP, diastolic blood pressure; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; HbA1c, hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; HT, hypertension; LDL-C, low-density lipoprotein cholesterol; NA, not applicable; SBP, systolic blood pressure.

Non-Lipid Residual Risk Factors and Clinical Outcomes

After adjusting for the effects of confounders, an eGFR ≤ 60 mL/min/1.73 m2 showed the highest risk of the primary outcome (Table 2, Fig.2A, Supplementary Fig.1A). In the eGFR ＞60 mL/min/1.73 m2 group, using antidiabetic medications were also significantly associated with the primary outcome. Using antihypertensive medications was significantly associated with secondary outcomes. Furthermore, the combination of antihypertensive medications and HbA1c ≥ 6.0% showed the highest risk of secondary outcomes (Table 2, Fig.2B, Supplementary Fig.1B). Both an eGFR ≤ 60 mL/min/1.73 m2 and HbA1c ＞6.0% were significant risk factors for all-cause death, CV death and cardiac death, with the highest risk when both factors were combined (Table 2, Figs.3A-C, Supplementary Figs.2A-C). In patients taking antihypertensive medications, eGFR ≤ 60 mL/min/1.73 m2 was also a significant risk factor for the primary outcome, all-cause death, CV death, and cardiac death (Table 3, Figs.4A and 5A–C, Supplementary Figs.3A and 4A-C). The combination of an eGFR ≤ 60 mL/min/1.73 m2 and HbA1c ≥ 6.0% also showed the highest risk of all-cause death and CV death. In the eGFR ＞60 mL/min/1.73 m2 group, using antidiabetic medications was also a significant risk factor for the primary outcome, all-cause death, and cardiac death. In contrast, the only risk factor for the secondary outcome was the HbA1c level ≥ 6.0% (Table 3, Fig.4B, Supplementary Fig.3B). In patients without antidiabetic medications, eGFR ≤ 60 mL/min/1.73 m2 was also the most important risk factor for all outcomes (Table 4, Figs.6A, B, and 7A-C, Supplementary Figs.5A, B, and 6A-C).

Table 2.Relationship between non-lipid risk factors and events in all studied patients

N= 8,743 N HR 95%CI SE P value
Primary outcome eGFR ＞60, no anti-DM medicine, SBP ≥ 120 2,757 ref
eGFR ＞60, no anti-DM medicine, SBP ＜120 1,176 1.61 1.12-2.32 0.30 .01
eGFR ＞60, anti-DM medicine 1,298 1.60 1.14-2.26 0.28 .007
eGFR ≤ 60 3,412 1.92 1.45-2.53 0.27 ＜.001
Secondary outcome No anti-HT medicine 1,451 ref
Anti-HT medicine, HbA1c ＜6.0% 4,794 1.34 1.06-1.70 0.16 .02
Anti-HT medicine, HbA1c ≥ 6.0% 2,498 1.55 1.20-1.99 0.20 .001
All cause death eGFR ＞60, HbA1c ＜6.0% 3,579 ref
eGFR ＞60, HbA1c ≥ 6.0% 1,752 1.61 1.12-2.31 0.30 .01
eGFR ≤ 60, HbA1c ＜6.0% 2,214 1.56 1.12-2.17 0.26 .009
eGFR ≤ 60, HbA1c ≥ 6.0% 1,198 2.42 1.72-3.41 0.42 ＜.001
Cardiovascular death eGFR ＞60 5,331 ref
eGFR ≤ 60, HbA1c ＜6.0% 2,214 1.29 0.82-2.02 0.29 .27
eGFR ≤ 60, HbA1c ≥ 6.0% 1,198 2.09 1.33-3.29 0.48 .001
Cardiac death eGFR ＞60, SBP ≥ 120, no anti- DM medicine 1,243 ref
eGFR ＞60, SBP ≥ 120, anti-DM medicine 1,035 3.63 1.38-9.56 1.79 .009
eGFR ＞60, SBP ＜120 1,539 4.03 1.65-9.83 1.83 .002
eGFR ≤ 60, HbA1c ＜6.0% 2,214 4.15 1.80-9.58 1.77 .001
eGFR ≤ 60, HbA1c ≥ 6.0% 1,198 6.80 2.94-15.74 2.91 ＜.001

Abbreviations: CI, confidence interval; HR, hazard ratio; ref, reference; SE, standard error; other abbreviations are as in Table 1.

Adjusted for Pitavastatin dose (1mg vs 4mg), age, sex, body mass index, LDL-C, HDL-C, triglyceride, C-reactive protein, and heart rate. eGFR (mL/min/1.73 m2), SBP (mmHg)

Fig.2. Kaplan-Meier curves for primary and secondary outcomes

eGFR, estimated glomerular filtration rate; DM, diabetes mellitus; SBP, systolic blood pressure; HR, hazard ratio; CI, confidence interval; HT, hypertension; HbA1c, hemoglobin A1c.

eGFR (mL/min/1.73 m2), SBP (mmHg)

Supplementary Fig.1. Classification and regression tree analyses for primary and secondary outcomes in all studied patients

eGFR, estimated glomerular filtration rate; HR, hazard ratio; DM, diabetes mellitus; SBP, systolic blood pressure; HT, hypertension; HbA1c, hemoglobin A1c.

eGFR (mL/min/1.73 m2), SBP (mmHg)

Fig.3. Kaplan-Meier curves for all-cause death, cardiovascular death, and cardiac death

eGFR, estimated glomerular filtration rate; HbA1c, hemoglobin A1c; HR, hazard ratio; CI, confidence interval; DM, diabetes mellitus; SBP, systolic blood pressure.

eGFR (mL/min/1.73 m2), SBP (mmHg)

Supplementary Fig.2. Classification and regression tree analyses for all-cause death, cardiovascular death, and cardiac death in all studied patients

eGFR, estimated glomerular filtration rate; HbA1c, hemoglobin A1c; HR, hazard ratio; SBP, systolic blood pressure; DM, diabetes mellitus.

eGFR (mL/min/1.73 m2), SBP (mmHg)

Table 3.Relationship between non-lipid risk factors and events in patients with anti-hypertensive medicine

N= 7,292 N HR 95%CI SE P value
Primary outcome eGFR ＞60, no anti-DM medicine 3,104 ref
eGFR ＞60, anti-DM medicine 1,243 1.44 1.03-2.00 0.24 .03
eGFR ≤ 60 2,945 1.62 1.25-2.09 0.21 ＜.001
Secondary outcome HbA1c ＜6.0% 4,794 ref
HbA1c ≥ 6.0% 2,498 1.16 0.98-1.36 0.10 .08
All cause death eGFR ＞60, no anti-DM medicine 3,104 ref
eGFR ＞60, anti-DM medicine 1,243 1.65 1.10-2.48 0.34 .01
eGFR ≤ 60, HbA1c ＜6.0% 1,903 1.56 1.10-2.21 0.28 .01
eGFR ≤ 60, HbA1c ≥ 6.0% 1,042 2.38 1.66-3.42 0.44 ＜.001
Cardiovascular death eGFR ＞60 4,347 ref
eGFR ≤ 60, HbA1c ＜6.0% 1,903 1.23 0.77-1.98 0.30 .39
eGFR ≤ 60, HbA1c ≥ 6.0% 1,042 1.92 1.19-3.11 0.47 .008
Cardiac death eGFR ＞60, no anti-DM medicine 3,104 ref
eGFR ＞60, anti-DM medicine 1,243 1.84 0.89-3.81 0.68 .09
eGFR ≤ 60 2,945 2.54 1.44-4.48 0.73 .001

Abbreviations as in Tables 1 and 2.

Adjustment factors are the same as those used in Table 2.

Fig.4. Kaplan-Meier curves for primary and secondary outcomes in the group that received antihypertensive medication

eGFR, estimated glomerular filtration rate; DM, diabetes mellitus; HR, hazard ratio; CI, confidence interval; HbA1c, hemoglobin A1c.

eGFR (mL/min/1.73 m2)

Fig.5. Kaplan-Meier curves for all-cause death, cardiovascular death, and cardiac death in the group that received antihypertensive medication

eGFR, estimated glomerular filtration rate; HbA1c, hemoglobin A1c; DM, diabetes mellitus; HR, hazard ratio; CI, confidence interval.

eGFR (mL/min/1.73 m2)

Supplementary Fig.3. Classification and regression tree analyses for primary and secondary outcomes in patients with anti-hypertensive medicine

HT, hypertension; eGFR, estimated glomerular filtration rate; HR, hazard ratio; DM, diabetes mellitus; HbA1c, hemoglobin A1c.

eGFR (mL/min/1.73 m2)

Supplementary Fig.4. Classification and regression tree analyses for all-cause death, cardiovascular death, and cardiac death in patients with anti-hypertensive medicine

HT, hypertension; eGFR, estimated glomerular filtration rate; HbA1c, hemoglobin A1c; DM, diabetes mellitus; HR, hazard ratio.

eGFR (mL/min/1.73 m2)

Table 4.Relationship between non-lipid risk factors and events in patients without anti-diabetic medicine

N= 6,377 N HR 95%CI SE P value
Primary outcome eGFR ＞60, SBP ≥ 120 2,757 ref
eGFR ＞60, SBP ＜120 1,176 1.59 1.10-2.29 0.30 .01
eGFR ≤ 60 2,444 1.85 1.38-2.49 0.28 ＜.001
Secondary outcome No anti-HT medicine 1,236 ref
Anti-HT medicine, eGFR ＞60 3,104 1.33 1.02-1.74 0.18 .03
Anti-HT medicine, eGFR ≤ 60 2,037 1.48 1.12-1.95 0.21 .006
All cause death eGFR ＞60, SBP ≥ 120 2,757 ref
eGFR ＞60, SBP ＜120 1,176 1.64 1.03-2.62 0.39 .03
eGFR ≤ 60 2,444 1.95 1.36-2.79 0.36 ＜.001
Cardiovascular death eGFR ＞60, SBP ≥ 120 2,757 ref
eGFR ＞60, SBP ＜120 1,176 2.50 1.26-4.99 0.88 .009
eGFR ≤ 60 2,444 2.45 1.36-4.41 0.73 .003
Cardiac death eGFR ＞60, SBP ≥ 120 2,757 ref
eGFR ＞60, SBP ＜120 1,176 4.27 1.69-10.78 2.02 .002
eGFR ≤ 60 2,444 4.57 2.00-10.48 1.94 ＜.001

Abbreviations as in Tables 1 and 2.

Adjustment factors are the same as those used in Table 2.

Fig.6. Kaplan-Meier curves for primary and secondary outcomes in the group without antidiabetic medication

eGFR, estimated glomerular filtration rate; SBP, systolic blood pressure; HR, hazard ratio; CI, confidence interval; HT, hypertension.

eGFR (mL/min/1.73 m2), SBP (mmHg)

Fig.7. Kaplan-Meier curves for all-cause death, cardiovascular death, and cardiac death in the group without antidiabetic medication

eGFR, estimated glomerular filtration rate; SBP, systolic blood pressure; HR, hazard ratio; CI, confidence interval.

eGFR (mL/min/1.73 m2), SBP (mmHg)

Supplementary Fig.5. Classification and regression tree analyses for primary and secondary outcomes in patients without anti-diabetic medicine

DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; HR, hazard ratio; SBP, systolic blood pressure; HT, hypertension; HbA1c, hemoglobin A1c.

eGFR (mL/min/1.73 m2), SBP (mmHg)

Supplementary Fig.6. Classification and regression tree analyses for all-cause death, cardiovascular death, and cardiac death in patients without anti-diabetic medicine

DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; HR, hazard ratio; SBP, systolic blood pressure; HR, hazard ratio; CI, confidence interval.

eGFR (mL/min/1.73 m2), SBP (mmHg)

Discussion

The main findings of the present study are summarized as follows: (1) An eGFR ≤ 60 mL/min/1.73 m2 showed the highest risk of the primary outcome consisting of CV death, nonfatal myocardial infarction, nonfatal stroke, or unstable angina pectoris requiring emergency hospitalization in patients with stable CAD who achieved LDL-C ＜100 mg/dL using pitavastatin regardless of using antihypertensive or diabetic medication; (2) The combination of eGFR ≤ 60 mL/min/1.73 m2 and HbA1c ≥ 6.0% was associated with the highest risk of all-cause death and CV death. These findings suggest that renal dysfunction and HbA1c levels could be the most important non-lipid residual risk factors for subsequent adverse events in patients who achieved guideline-directed LDL-C levels as a secondary prevention.

Renal Dysfunction as a Residual Risk

In the current study, renal dysfunction (eGFR ≤ 60 mL/min/1.73 m2) was the strongest non-lipid residual risk factor for adverse events. Although clinical trials commonly excluded patients with impaired renal function, prior to the strong recommendation of aggressive lipid-lowering statin therapy, CKD was noted as a risk factor for worse clinical outcomes after percutaneous coronary artery intervention (PCI) in patients with ischemic heart disease19). According to the Framingham study, CKD has a high rate of complications of traditionally established risk factors, such as hypertension, diabetes and dyslipidemia; therefore, the probability of the occurrence of several cardiac CV events is high in those with CKD20). In several observational studies of patients with ACS or stable CAD who underwent PCI, the risk of CV events was higher in patients with mild CKD (eGFR 60-90 mL/min/1.73 m2) and moderate or severe CKD (eGFR 60 mL/min/1.73 m2) than in those without CKD (eGFR ≥ 90 ml/min/1.73 m2), suggesting that the risk of a CV event increases with worsening eGFR values21-23). Renal dysfunction could be one of the key factors associated with CV events despite aggressive lipid lowering therapy with statins9). Moreover, an intravascular ultrasound study of lipid plaque volume and histological changes in non-culprit coronary artery lesions under statin treatment showed that CKD patients had plaque progression, and histologically evident decrease in fibrous components and increase in lipid components24). Chronic inflammation is an important component of CKD and has an important role in the pathophysiology of cardiovascular disease (CVD), as well as protein energy deficiency and mortality25, 26). Multiple factors contribute to the setting of the inflammatory status in CKD, including increased production of pro-inflammatory cytokines, oxidative stress, chronic and recurrent vascular access infections, altered metabolism of adipose tissue, and gut microbiota dysbiosis. Other studies also indicated that coronary plaques in CKD cases had more severe inflammatory changes than those in non-CKD controls27). Given these features, the effect of statins on plaque regression would be weaker in patients with CKD than those with non-CKD. In a post hoc analysis of the Canakinumab Anti-inflammatory Thrombosis Outcome Study, canakinumab, which has anti-inflammatory properties, reduced adverse events in patients with CKD (eGFR ＜60 mL/min/1.73 m2) who exhibited a high incidence of CV events28). For further reduction of CV events in patients with renal dysfunction, novel treatments targeting inflammation, in addition to intensive lipid-lowering therapy, are warranted.

Glycemic Control as a Residual Risk

In our study, HbA1c levels ≥ 6.0% were independently associated with worse clinical outcomes. Furthermore, a higher HbA1c level has an incremental prognostic value for renal dysfunction. Several observational studies have indicated high mortality and recurrent event rates in patients with CV disease complicated by diabetes29, 30). In previous intervention studies focusing on strict glycemic control, the onset of a CV event could not be suppressed, even with several years of interventional treatment. In particular, in the study Action to Control Cardiovascular Risk in Diabetes, which focused on the effects of intensive glucose lowering in type 2 diabetes, a significant increase in mortality was observed in the intensive therapy group, possibly due to hypoglycemia31). The present study included patients with and without diabetes, of whom 27% used antidiabetic medications, and approximately 70% of patients did not have diabetes. Therefore, the association between HbA1c level and CVD is less clear, with few data reported in patients without diabetes mellitus. Khaw reported that increasing HbA1c levels are associated with all-cause and CV mortality in non-diabetic patients32). In that an increase in HbA1c of 1 percentage point was associated with a relative risk of all-cause mortality of 1.26 (95% CI, 1.04 to 1.52; P=0.02). Furthermore, a meta-analysis study showed that the optimal level of HbA1c for the general population without diabetes was in the range of 5.0% to 6.0%; furthermore, beyond 6.0%, the risk of CV death increased significantly31). Our current findings are consistent with those of previous studies, suggesting the role of HbA1c assessment in risk stratification in patients with or without diabetes. However, our previous REAL-CAD analysis, which focused on non-lipid residual risk in patients who received high-dose (4 mg/day) pitavastatin, showed that baseline HbA1c was not significantly associated with CV events16). The main reason might be due to a difference in statin dose. In this study, the proportion of patients who received high-dose (4 mg/dl) and low-dose (1 mg/dl) pitavastatin was almost same (50% vs. 50%). This could be due to a difference in statin dose. Despite the safety and relative tolerability of statins, multiple meta-analyses found that statins were associated with the development of diabetes, particularly among those with predisposing risk factors for diabetes33-36). Several studies implicated that statins had a negative impact on insulin sensitivity and secretion and increased insulin resistance37, 38). In particular, the risk of developing diabetes and worsening of glycemic control was higher in patients taking higher dose statins compared to those taking lower dose statins33). On the other hand, recent studies have investigated dose-dependent effects and the intensity of statin use on all-cause mortality in patients with type 2 diabetes39). In these studies, the level of HbA1c might not be a risk factor, but these included only patients receiving high-dose statins. When LDL-C levels are well controlled, low-dose statins are often used owing to concerns about side effects. Therefore, our findings would be reasonable in identifying non-lipid residual risk factors in the real world population.

Strengths and Limitations

This study revealed strong and robust non-lipid residual risk factors, such as eGFR and HbA1c levels, on adverse events in patients with stable CAD who achieved guideline-directed LDL-C levels as secondary prevention using a large trial cohort. There are several potential limitations of the present study that should be acknowledged. First, this study used a post hoc analysis. Second, selection bias could not be avoided, as 3,670 (29.5%) of the 12,413 patients in the REAL-CAD study were excluded.

Third, the REAL-CAD study did not collect the information regarding the type of diabetes and insulin use; therefore, we could not consider these factors for the analyses. Fourth, high levels of triglycerides could be linked to adverse CV events even under optimal LDL-C control with statins. This fact can potentially lead to bias toward higher CV risk in the higher triglycerides group40, 41). However, in the multivariable models, we adjusted for baseline triglyceride levels, resulting in reducing the impact of potential false bias due to triglyceride control status. Fifth, detailed information about the underlying renal dysfunction and the amount of urinary albumin was not available. Finally, there may have been other differences in the variables not evaluated but could have affected the results of the analysis.

Conclusions

In pitavastatin-treated patients with stable CAD who achieved guideline-directed LDL-C levels as secondary prevention (＜100 mg/dL), renal dysfunction and HbA1c levels could be strong non-lipid residual risk factors that contribute to CV events.

Acknowledgements

None.

Sources of Funding

This study was funded by the Comprehensive Support Project for Clinical Research of Lifestyle-Related Diseases of the Public Health Research Foundation. The company manufacturing the study drug (Kowa Pharmaceutical Co., Ltd.) was one of the entities providing financial support for Public Health Research Foundation projects, but was not involved in the design, analysis, data interpretation, or manuscript preparation.

CRediT Authorship Contribution Statement

KK, MT, TN, and YF designed the study. YF, HO, and TK analyzed and interpreted the data. KK, MT, and TN were the major contributors to writing the manuscript. TM, HI, YF, IS, HD, SI, HS, TK, RN, and TA made substantial revisions to the manuscript. All authors have read and approved the final manuscript. HI and RN are members of the Editorial Team of the Journal of Atherosclerosis and Thrombosis.

Conflict of Interest Statement

All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr. T Nagai received a research grant from Mitsubishi Tanabe Pharma Corp. and honoraria from Kyowa Kirin Co., Ltd, Bayer Yakuhin, Ltd., Viatris Inc., Boehringer Ingelheim Japan Co., Ltd., and Novartis AG. Dr Ito received research grant and honoraria from Kowa Pharmaceutical Co Ltd, and Mochida Pharmaceutical Co, Ltd. Dr. Fukumoto received research grants from Sanofi KK, Shionogi & Co, Ltd, honoraria from Public Health Research Foundation, AstraZeneca KK, Eisai Co, Ltd, Kowa Pharmaceutical Co Ltd, research grant and honoraria from MSD KK, Otsuka Pharmaceutical Co, Ltd, Daiichi Sankyo Co, Ltd, Sumitomo Dainippon Pharma Co, Ltd, Teijin Pharma Ltd, Bayer Yakuhin, Ltd, Mochida Pharmaceutical Co, Ltd, Astellas Pharma Inc, Sanwa Kagaku Kenkyusho Co, Ltd, Takeda Pharmaceutical Co. Ltd, Mitsubishi Tanabe Pharma Corp, Pfizer Japan Inc., Ono Pharmaceutical Co Ltd, and AstraZeneca KK. Dr. Sakuma received honoraria from Bayer Yakuhin, Ltd.; other research support from Takeda Pharmaceutical Co., Ltd.; and a research grant and other research support from the Public Health Research Foundation. Dr. Daida received honoraria from Amgen Astellas BioPharma KK, Daiichi Sankyo Co. Ltd., Kowa Pharmaceutical Co. Ltd., and MSD KK; research grants from Canon, Glory, and FUJIFILM Holdings Co. Ltd.; scholarship grants from Eisai Co. Ltd., Bayer Yakuhin, Ltd., and Daiichi Sankyo Co. Ltd.; and courses endowed by Phillips, Resmed, Fukuda Denshi, Asahikasei, Inter-Reha, and Toho Holdings Co. Ltd. Dr. Iimuro received a research grant from Amgen Astellas BioPharma KK, and a research grant and honoraria from the Public Health Research Foundation. Dr. Shimokawa received research grants from Shionogi & Co. Ltd., Teijin Pharma Ltd., Astellas Pharma Inc., Otsuka Pharmaceutical Co. Ltd.; honoraria from Kowa Pharmaceutical Co. Ltd., Sanofi KK, AstraZeneca KK, and Bayer Yakuhin, Ltd.; research grants and honoraria from MSD KK, Mitsubishi Tanabe Pharma Corp., and Daiichi Sankyo Co. Ltd.; and research grants, other research support, and honoraria from the Public Health Research Foundation. Dr. Kimura received research grants from Sumitomo Dainippon Pharma Co. Ltd., Astellas Pharma Inc., Otsuka Pharmaceutical Co. Ltd., Mitsubishi Tanabe Pharma Corp., and Takeda Pharmaceutical Co. Ltd.; other research support and honoraria from Kowa Pharmaceutical Co. Ltd., Bayer Yakuhin, Ltd.; and research grants, other research support, and honoraria from MSD KK, Sanofi KK, Mochida Pharmaceutical Co. Ltd., Daiichi Sankyo Co. Ltd., the Public Health Research Foundation, and Amgen Astellas BioPharma KK. Dr. R Nagai received honoraria from Kowa Pharmaceutical Co. Ltd., Takeda Pharmaceutical Co. Ltd., Bayer Yakuhin, Ltd., Daiichi Sankyo Co. Ltd., Shionogi & Co. Ltd., MSD KK, Mitsubishi Tanabe Pharma Corp., Amgen Astellas BioPharma KK, Eisai Co. Ltd., Astellas Pharma Inc., Sumitomo Dainippon Pharma Co. Ltd., and Mochida Pharmaceutical Co. Ltd. and honoraria and expert witness fees from the Public Health Research Foundation. Dr. Anzai received a research grant from the Japan Agency for Medical Research and Development, a research grant from Daiichi Sankyo Co., Ltd., scholarship funds from Biotronik Japan Co., Ltd., Medtronic Japan Co., Ltd., Win International Co., Ltd., Medical System Network Co., Ltd., and Hokuyaku Takeyama Holdings, Inc., and honoraria from Daiichi Sankyo Co., Ltd., Ono Pharmaceutical Co., Ltd., Boehringer Ingelheim Japan Co., Ltd., Bayer’s Pharmaceuticals Co., Ltd., and Bristol-Myers Squibb Co., Ltd. The remaining authors declare no conflicts of interest.

References
 

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