Triglycerides and the Risk of Atherosclerotic Cardiovascular Events Across Different Risk Categories

Abstract

Aims: To investigate the association between triglyceride levels and major adverse cardiovascular events (MACE) in primary and secondary prevention cohorts.

Methods: This retrospective study was conducted with a nationwide health insurance claims database, which included approximately 3.8 million participants with medical checkups between January 2005 and August 2020 in Japan. The participants were classified into primary prevention (n=3,415,522) and secondary prevention (n=29,806) cohorts based on cardiovascular or cerebrovascular disease history. Each participant was categorized as having very low (triglyceride ＜50 mg/dL), low normal (50–99), high normal (100–149), or hypertriglyceridemia (≥ 150). The primary prevention cohort was further stratified into low-, intermediate-, and high-risk groups according to atherosclerotic cardiovascular diseases risk. Outcome was MACE, including acute myocardial infarction (AMI), unstable angina, ischemic stroke, and cardiac death.

Results: Over a mean follow-up of 3.25 years, 0.3% and 2.6% MACE occurred in primary and secondary prevention, respectively. Hypertriglyceridemia was associated with high risk of MACE in the primary prevention, but not in the secondary prevention. A significant interaction was observed between prevention categories and the association of TG levels with MACE in those with TG ＜150 mg/dL and ischemic stroke in those with TG ≥ 150 mg/dL. The population-attributable fraction for hypertriglyceridemia in primary prevention was 4.1% for MACE. In primary prevention, lower risks of AMI were observed in the lower TG category compared to the current threshold.

Conclusions: This study suggests distinct triglyceride thresholds for MACE risk in primary and secondary prevention cohorts, requiring further prospective validation for clinical implementation.

Hiroyuki Mizuta and Masanobu Ishii contributed equally to this work.

See editorial vol. 32: 783-785

Introduction

Managing dyslipidemia, particularly elevated low-density lipoprotein cholesterol (LDL-C), is key to preventing atherosclerotic diseases such as coronary artery disease (CAD) and ischemic stroke^1-3). Statin therapy is a standard treatment for lowering LDL-C, and reports indicate that further reductions in LDL-C decrease major cardiovascular events (MACE) risk, without any observed threshold^{4, 5)}. Add-on or alternative drugs to statins, including ezetimibe, bempedoic acid, PCSK9 inhibitors, further diminish cardiovascular risk^6-10). Although LDL-C lowering therapy is effective, there are residual risks from other lipids like triglycerides (TGs)^11-15).

TG-lowering therapy utilizing fibrates and niacin exhibits a mild LDL-lowering effect; however, previous randomized controlled trials (RCT) do not provide substantial support for their adjunctive use alongside statin therapy^{12, 16)}. The JELIS and REDUCE-IT trials demonstrated the effects of eicosapentaenoic acid (EPA) administration on MACE, which might not be explained solely by the effect of lowering TG^{17, 18)}. These evidences regarding TG-lowering therapy for MACE prevention is controversial in the statin era^{12, 16-18)}. A key aspect of this controversy lies in the potential variability of optimal TG thresholds for cardiovascular event reduction, which may differ between risk categories. While LDL-C targets are stratified by prevention type and risk level, the guidelines apply a uniform threshold of TG levels across both primary and secondary prevention^19-22). This uniform approach raises the question of whether a more tailored, risk-based TG management strategy is needed.

Aim

This study aimed to investigate the association between TG levels and the risk of atherosclerotic events and the interaction between those risks and prevention categories using a nationwide database.

Methods

The study protocol was approved by the Institutional Review Board of Kumamoto University Hospital (Rinri No. 2666) and was conducted in accordance with the principles outlined in the Declaration of Helsinki. The need for informed consent from the individual participants was waived because all data were de-identified (anonymized). This study adhered to the STROBE reporting guideline for observational studies.

Study Design, Participants, and Settings

This study was a retrospective analysis of the Japan Medical Data Center (JMDC) Claims Database, which is a nationwide health insurance claims database. It comprises de-identified individual healthcare records (inpatient, outpatient, dispensing) including demographic details, diagnoses categorized under the International Classification of Diseases, 10th Revision (ICD-10) coding, medication information, medical procedures, mortality data, hospitalization events, and annual health checkup data for workplace employees, inclusive of blood pressure readings and lipid profile laboratory results from multiple health associations, as well as Specific Health Checkups^23-26). Between January 2005 and August 2020, 3,824,093 participants who underwent a comprehensive medical checkup with measurement of lipid profiles such as LDL-C, high-density lipoprotein cholesterol (HDL-C), and TG were enrolled. Of these, analyzed population (n=3,445,328) was identified after exclusion of participants who had missing values of past histories (n=269,788) and data for antihyperlipidemic medications (n=108,977). The participants were classified as the primary prevention (n=3,415,522) and secondary prevention cohort (n=29,806) based on the prior history of cardiovascular disease such as angina pectoris, myocardial infarction, or coronary revascularization, and cerebrovascular disease such as ischemic stroke, or intracranial hemorrhage (Supplementary Fig.1).

Supplementary Fig.1.Study Flowchart According to Prevention Category

JMDC indicates Japan Medical Data Center.

Variables

During the health check-up, blood samples were collected from each participant who had fasted for more than 10 hours. Blood laboratory test data, such as TG, LDL-C, HDL-C, glucose, and hemoglobin A1C levels at the initial health checkup, were used for exposure. The TG category was divided into: 1) two groups using a cut-off value of 150 mg/dL based on current guidelines^19-21), or 2) four groups as follows: very low (＜50 mg/dL), low normal (50–99 mg/dL), high normal (100–149 mg/dL), or hypertriglyceridemia (≥ 150 mg/dL), as previously reported²⁷⁾ with tailored modifications. Following ＞5 min of seated rest, the brachial blood pressure was measured twice with a ＞1-min interval while an average of 2 measurements was used for analysis^{23, 28)}. Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or the use of anti-hypertensive agents^{23, 28)}. Diabetes mellitus was defined as a fasting plasma glucose level of ≥ 126 mg/dL or the use of anti-diabetic agents^{23, 24)}. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m²). The BMI categories were defined as underweight (BMI ＜18.5 kg/m²), normal weight (BMI 18.5 to ＜25 kg/m²), overweight (BMI 25 to ＜30 kg/m²), and obesity (BMI ≥ 30 kg/m²). The original questionnaire regarding smoking and alcohol consumption has been previously reported²⁹⁾. Briefly, current smoking status was assessed with the question: “Are you a current regular smoker?” (defined as having smoked 100 or more cigarettes or for at least six months, and currently smoking). Alcohol consumption was assessed by asking, “How often do you drink?” with response options of Rarely, Occasionally, or Everyday.

Outcomes

The primary outcome was MACE defined as AMI (ICD-10 codes: I21.0, I21.1, I21.2, I21.3, I21.4, and I21.9), a hospitalization for unstable angina (ICD-10 code: I20.0), ischemic stroke (ICD-10 codes: I63.0, I63.1, I63.2, I63.3, I63.4, I63.5, I63.6, I63.8, I63.9, I69.3), and cardiac death^{23, 30)}. Cardiac death included cardiocerebrovascular- or acute heart failure (ICD-10 codes: I50.0 and I50.9)- related death. If a participant experienced two or more events, the first was counted as the outcome. Secondary outcomes were defined as AMI, ischemic stroke, or all-cause death and were analyzed separately. These outcomes were assessed from the date of the health checkup to August 2020.

Statistical Analyses

Cox proportional hazards regression analysis treating TG as a continuous variable was used to compute the hazard ratios (HRs) and 95% confidence intervals (CIs) of clinical outcomes associated with TG levels. In addition, to reveal a potential non-linear relationship between TG levels and clinical outcomes, Cox hazard proportional regression model with natural cubic spline regression was performed with a reference TG level of 150 mg/dL. Interaction analyses were conducted to elucidate the distinct elevated TG level effects on clinical outcomes within the primary and secondary preventive contexts. Multivariable models were adjusted for potential confounding factors such as age, sex, BMI, current smoking status, hypertension, diabetes mellitus, LDL-C level, HDL-C level and statin use. To assess the interaction between primary and secondary prevention cohorts, we combined the datasets of all participants. An interaction term between prevention category (primary vs. secondary) and TG levels was included in the regression models. This combined analysis allowed us to evaluate the differential effects of TG levels on clinical outcomes across the two prevention categories. The interaction results were analyzed using multivariable-adjusted models. The population-attributable fraction (PAF) for clinical outcomes associated with high TG levels were computed. The PAF is a crucial tool in epidemiology to assess the impact of exposure on public health³¹⁾. It quantifies how reducing risk factors may decrease disease occurrence. The PAF highlights potential health improvements by moving from current exposure to an ideal state. The PAF (%) was calculated using the Miettinen formula, as previously described³²⁾. PAF was defined as exposure proportion among cases×(adjusted HR - 1) /adjusted HR×100. Adjusted HR values were derived from multivariable models to account for potential confounders. The PAF for clinical outcomes associated with higher TG levels (≥ 50 mg/dL), compared to the very low TG group (＜50 mg/dL), was calculated as the sum of the PAFs for each of the three higher TG categories³²⁾. For the sensitivity analysis, an iterative imputer method for missing values was used with 10 iterations of Bayesian Ridge. In primary prevention, subgroup analyses were performed to investigate the influence of TG levels on outcomes using three risk categories (low, intermediate, high risk of atherosclerotic cardiovascular disease) according to the Japan Atherosclerosis Society Guidelines for Prevention of Atherosclerotic Cardiovascular Diseases 2022 ²²⁾, or statins use. The level of significance was defined as a two-sided p-value of ＜0.05. All statistical analyses were performed using R software version 4.0.5. (R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.) and Python (V.3.7.11, Python Software Foundation).

Results

Participants Characteristics in Primary and Secondary Prevention

For 15 years, 3,415,522 participants were involved in primary prevention, with a median age of 44 years and 69% were men. Additionally, 29,806 participants engaged in secondary prevention, with a median age of 58 years, and 92% were men. In the secondary prevention group, 76% and 30% had a history of cardiovascular disease and cerebrovascular disease, respectively. A summary of baseline characteristics is presented in Table 1. In the primary prevention group, participants exhibited lower BMI, waist circumference, systolic and diastolic blood pressures, and a lower prevalence of hypertension and diabetes mellitus; however, a higher proportion smoked, compared to those in the secondary prevention group.

Table 1.Baseline characteristics of primary and secondary prevention cohorts

	missing, %	Overall N= 3,445,328	Primary Prevention N= 3,415,522	Secondary Prevention N= 29,806	p value
Age, years	0	45 (36, 53)	44 (36, 53)	58 (53, 63)	＜0.001
Male, n (%)	0	2,390,026 (69%)	2,362,627 (69%)	27,399 (92%)	＜0.001
Body mass index, kg/m2	0.02	22.7 (20.5, 25.3)	22.7 (20.5, 25.3)	25.2 (23.1, 27.8)	＜0.001
＜18.5		254,235 (7.4%)	253,927 (7.4%)	308 (1.0%)
≥ 18.5, ＜25		2,239,375 (65%)	2,225,800 (65%)	13,575 (46%)
≥ 25, ＜30		757,414 (22%)	745,409 (22%)	12,005 (40%)
≥ 30		193,719 (5.6%)	189,803 (5.6%)	3,916 (13%)
Waist circumference, cm	3.85	81 (75, 88)	81 (75, 88)	89 (83, 96)	＜0.001
Systolic blood pressure, mmHg	0.02	119 (109, 129)	119 (109, 129)	126 (116, 136)	＜0.001
Diastolic blood pressure, mmHg	0.02	74 (66, 82)	74 (66, 82)	78 (71, 85)	＜0.001
Hypertension, n (%)	0.003	344,823 (10%)	322,934 (9.5%)	21,889 (73%)	＜0.001
Diabetes, n (%)	0.005	108,801 (3.2%)	101,163 (3.0%)	7,638 (26%)	＜0.001
Current smoking, n (%)	0.09	968,008 (28%)	962,386 (28%)	5,622 (19%)	＜0.001
Alcohol consumption, n (%)	1.37				＜0.001
Everyday		850,046 (25%)	841,859 (25%)	8,187 (28%)
Occasionally		1,291,925 (38%)	1,282,457 (38%)	9,468 (32%)
Rarely		1,256,145 (37%)	1,244,263 (37%)	11,882 (40%)
History of cerebrovascular disease, n (%)	0	8,817 (0.3%)	0 (0%)	8,817 (30%)	＜0.001
History of cardiovascular disease, n (%)	0	22,581 (0.7%)	0 (0%)	22,581 (76%)	＜0.001
Glucose, mg/dL	3.84	92 (86, 99)	92 (86, 99)	103 (94, 118)	＜0.001
HbA1c, %	10.55	5.40 (5.20, 5.60)	5.40 (5.20, 5.60)	5.90 (5.50, 6.40)	＜0.001
LDL-C, mg/dL	0	119 (98, 141)	119 (99, 141)	99 (83, 118)	＜0.001
HDL-C, mg/dL	0	60 (50, 72)	60 (51, 72)	53 (45, 63)	＜0.001
TG, mg/dL	0	84 (58, 127)	84 (58, 127)	111 (80, 160)	＜0.001
Hypertriglyceridemia (TG ≥ 150 mg/dL), n (%)	0	609,199 (18%)	600,575 (18%)	8,624 (29%)	＜0.001
Statins, n (%)	0	237,706 (6.9%)	212,015 (6%)	25,691 (86%)	＜0.001
Ezetimibe, n (%)	0	26,313 (0.8%)	22,022 (0.6%)	4,291 (14%)	＜0.001
PCSK9 inhibitor, n (%)	0	175 (＜0.1%)	51 (＜0.1%)	124 (0.4%)	＜0.001
Fibrate, n (%)	0	47,270 (1.4%)	44,468 (1.3%)	2,802 (9.4%)	＜0.001
Omega-3 carboxylic acids, n (%)	0	9,049 (0.3%)	8,191 (0.2%)	858 (2.9%)	＜0.001
EPA, n (%)	0	16,323 (0.5%)	13,944 (0.4%)	2,379 (8.0%)	＜0.001

TG, triglyceride; HbA1c, hemoglobin A1c; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; PCSK9, proprotein convertase subtilisin/kexin type9; EPA, eicosapentaenoic acid.

Association between TG Levels and Clinical Outcomes

During a mean follow-up of 3.25±2.53 years, 11,672 first MACE, 4,320 AMI, 2,612 unstable angina, 4,626 ischemic stroke, and 5,170 all-cause death occurred (Supplementary Table 1). As shown in Table 2, TG levels as a continuous variable were significantly associated with MACE in both primary and secondary prevention cohorts (HR: 1.008, 95% CI: 1.006–1.010 in the primary prevention cohort, and HR: 1.008, 95% CI: 1.002–1.014 in the secondary prevention cohort). For AMI, TG levels were significantly associated with risk in the primary prevention cohort (HR: 1.007, 95% CI: 1.004–1.009), but not in the secondary prevention cohort (HR: 1.004, 95% CI: 0.994–1.015). For ischemic stroke, TG levels were significantly associated with risk in both the primary (HR: 1.007, 95% CI: 1.004–1.010) and secondary prevention cohorts (HR: 1.018, 95% CI: 1.010–1.026). No significant association was observed between TG levels and all-cause death in either the primary (HR: 1.000, 95% CI: 0.998–1.003) or secondary (HR: 1.008, 95% CI: 0.995–1.022) prevention cohorts. In addition, Table 3 presents the categorical analysis using TG 150 mg/dL as the cutoff point. The results show that in the primary prevention cohort, TG levels ≥ 150 mg/dL were significantly associated with an increased risk of MACE (HR: 1.13, 95% CI: 1.09-1.18), AMI (HR: 1.13, 95% CI: 1.06-1.21), and ischemic stroke (HR: 1.11, 95% CI: 1.03-1.19) compared to TG levels ＜150 mg/dL. However, in the secondary prevention cohort, no significant association was observed for these outcomes (HR: 1.08, 95% CI: 0.92-1.26 for MACE; HR: 1.11, 95% CI: 0.83-1.47 for AMI; HR: 1.23, 95% CI: 0.91-1.66 for ischemic stroke). No significant association was observed between TG levels and all-cause death in either the primary (HR: 1.05, 95% CI: 0.98–1.13) or secondary (HR: 0.97, 95% CI: 0.66–1.43) prevention cohorts (Table 3).

Supplementary Table 1.Clinical Outcomes in Primary and Secondary Prevention

	Overall N= 3,445,328	Primary Prevention N= 3,415,522	Secondary Prevention N= 29,806
Major adverse cardiovascular event	11,672 (0.3%)	10,906 (0.3%)	766 (2.6%)
Acute myocardial infarction	4,320 (0.1%)	4,093 (0.1%)	227 (0.8%)
Unstable angina	2,612 (＜0.1%)	2,274 (＜0.1%)	338 (1.1%)
Ischemic stroke	4,626 (0.1%)	4,419 (0.1%)	207 (0.7%)
All-cause death	5,170 (0.2%)	5,033 (0.1%)	137 (0.5%)

Data are n (%).

Table 2.Estimation for hazard risk for clinical outcome using triglycerides as a continuous variable

	number of participants, n	number of event, n (%)	Multivariable				Iterative imputation
			HR	95% CI low	95% CI high	p value	HR	95% CI low	95% CI high	p value
MACE
Total cohort	3,445,328	11,672 (0.3%)	1.008	1.006	1.010	＜0.001	1.008	1.006	1.010	＜0.001
Primary prevention	3,415,522	10,906 (0.3%)	1.008	1.006	1.010	＜0.001	1.008	1.006	1.010	＜0.001
Secondary prevention	29,806	766 (2.6%)	1.008	1.002	1.014	0.005	1.008	1.002	1.014	0.005
AMI
Total cohort	3,445,328	4,320 (0.1%)	1.006	1.004	1.009	＜0.001	1.007	1.004	1.009	＜0.001
Primary prevention	3,415,522	4,093 (0.1%)	1.007	1.004	1.009	＜0.001	1.007	1.004	1.009	＜0.001
Secondary prevention	29,806	227 (0.8%)	1.004	0.994	1.015	0.405	1.004	0.994	1.015	0.402
Ischemic stroke
Total cohort	3,445,328	4,626 (0.1%)	1.008	1.005	1.010	＜0.001	1.008	1.005	1.010	＜0.001
Primary prevention	3,415,522	4,419 (0.1%)	1.007	1.004	1.010	＜0.001	1.007	1.004	1.010	＜0.001
Secondary prevention	29,806	207 (0.7%)	1.018	1.010	1.026	＜0.001	1.018	1.010	1.026	＜0.001
All-cause death
Total cohort	3,445,328	5,170 (0.2%)	1.001	0.998	1.003	0.639	1.001	0.998	1.003	0.645
Primary prevention	3,415,522	5,033 (0.1%)	1.000	0.998	1.003	0.801	1.000	0.998	1.003	0.807
Secondary prevention	29,806	137 (0.5%)	1.008	0.995	1.022	0.208	1.008	0.995	1.022	0.207

HR was adjusted for age, sex, body mass index, smoking habit, hypertension, diabetes mellitus, LDL-C level, HDL-C level and statin use. HR, hazard ratio; MACE, major adverse cardiac events; AMI, acute myocardial infarction.

Table 3.Estimation for hazard risk and population attributable fraction

	Primary Prevention					Secondary Prevention
	Number of participants	Number of event	Incidence rate (95% CI) (per 100,000 person-year)	HR (95% CI)	PAF (%)	Number of participants	Number of event	Incidence rate (95% CI) (per 100,000 person-year)	HR (95% CI)	PAF (%)
MACE
TG ＜150 mg/dL	2,814,947	7,136	79.1 (78.5, 79.7)	Ref	Ref	21,182	492	814 (794, 835)	Ref	Ref
TG ≥ 150 mg/dL	600,575	3,770	181.7 (179.7, 183.7)	1.13 (1.09, 1.18)	4.1	8,624	274	1062 (1025, 1100)	1.08 (0.92, 1.26)	NA
AMI
TG ＜150 mg/dL	2,814,947	2,439	27.0 (26.7, 27.3)	Ref	Ref	21,182	138	225 (214, 236)	Ref	Ref
TG ≥ 150 mg/dL	600,575	1,654	79.5 (78.2, 80.9)	1.13 (1.06, 1.21)	4.8	8,624	89	339 (318, 361)	1.11 (0.83, 1.47)	NA
Ischemic stroke
TG ＜150 mg/dL	2,814,947	3,099	34.3 (33.9, 34.7)	Ref	Ref	21,182	130	212 (201, 223)	Ref	Ref
TG ≥ 150 mg/dL	600,575	1,320	63.4 (62.2, 64.6)	1.11 (1.03, 1.19)	2.9	8,624	77	293 (273, 313)	1.23 (0.91, 1.66)	NA
All-cause death
TG ＜150 mg/dL	2,814,947	3,758	41.6 (41.2, 42.0)	Ref	Ref	21,182	95	154 (145, 163)	Ref	Ref
TG ≥ 150 mg/dL	600,575	1,275	61.2 (60.0, 62.4)	1.05 (0.98, 1.13)	NA	8,624	42	159 (144, 174)	0.97 (0.66, 1.43)	NA

HR was adjusted for age, sex, body mass index, smoking habit, hypertension, diabetes mellitus, LDL-C level, HDL-C level and statin use.

HR, hazard ratio; PAF, population-attributable fraction; MACE, major adverse cardiac events; AMI, acute myocardial infarction; TG, triglyceride.

As shown in Fig.1, the multivariable Cox hazard proportional regression model with natural cubic spline regression revealed that the association of lower TG levels with a lower risk of MACE was more evident in the primary prevention than in the secondary prevention group within the range of less than 150mg/dL (p for interaction; 0.049). Regarding ischemic stroke incidence, the association of lower TG levels with a lower risk of ischemic stroke was more evident in the secondary prevention than in the primary prevention group within the range of more than 150mg/dL (p for interaction; 0.030).

Fig.1. Association Between Triglyceride Levels and the Risk of Clinical Outcomes Stratified by Risk Category

Line graphs reveal the spline curves of adjusted hazard ratios (solid lines) and 95% confidence intervals (translucent areas) for (a) MACE, (b) AMI, (c) stroke, and (d) all-cause death in primary (blue) and secondary (red) prevention. Hazard ratios were adjusted for age, sex, body mass index, smoking habit, hypertension, diabetes mellitus, LDL-C level, HDL-C level, and statin use.

TG, triglyceride; MACE, major adverse cardiovascular event; AMI, acute myocardial infarction, LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol.

Population Attributable Fraction of Clinical Outcomes Associated with High TG Levels

The estimated incidence rates of clinical outcomes and PAF of clinical outcomes associated with hypertriglyceridemia are presented in Table 3. In primary prevention, the PAFs were 4.1% for MACE, 4.8% for AMI, and 2.9% for ischemic stroke. In the secondary prevention cohort, however, the adjusted HRs for TG levels ≥ 150 mg/dL were not statistically significant, and thus the PAFs were not presented due to the absence of a meaningful association (Table 3).

Exploring Clinical Outcome Risks in Primary Prevention beyond the TG Level Threshold

The characteristics and clinical outcomes in primary prevention are summarized according to four TG categories in Supplementary Tables 2-3. The Kaplan–Meier curve provided an estimate of the cumulative incidence of clinical outcomes within these four TG category groups (Supplementary Fig.2). Additionally, Fig.2 indicates the association between the four TG categories and the risk of clinical outcomes in primary and secondary prevention groups. In the primary prevention group, the multivariable Cox hazard proportion regression model indicated that both high normal TG group and hypertriglyceridemia were associated with a higher risk of AMI compared with the very low TG group (HR: 1.24, 95% CI: 1.01–1.54 for the high normal TG group, HR: 1.36, 95% CI: 1.10-1.69 for hypertriglyceridemia). Regarding MACE and ischemic stroke, hypertriglyceridemia was associated with higher risk compared with the very low TG group (HR: 1.15, 95% CI: 1.04–1.28 for MACE, HR: 1.20, 95% CI: 1.04-1.40 for ischemic stroke). However, these association were not significant in the secondary prevention. The findings of the iterative imputation analysis, which served as a sensitivity analysis, were consistent with the main results (Fig.2). In the primary prevention group, the PAFs for MACE, AMI, and ischemic stroke associated with TG levels ≥ 50 mg/dL, compared to the very low TG group (＜50 mg/dL), were 5.48%, 20.4%, and 10.5%, respectively (Supplementary Table 4). In the secondary prevention cohort, however, the adjusted HRs for TG levels ≥ 50 mg/dL were not statistically significant, and thus the PAFs were not calculated due to the absence of a meaningful association (Supplementary Table 4).

Supplementary Table 2.Baseline Characteristics of Primary Prevention Divided by the Four Triglyceride Category Groups

	Missing, n (%)	Overall N=3,415,522	Very low (＜50 mg/dL) N=545,535	Low Normal (50 to 99 mg/dL) N=1,553,942	High Normal (100 to 149 mg/dL) N=715,470	Hypertriglyceridemia (≥ 150 mg/dL) N=600,575	p-value
Age, years	0 (0%)	44 (36, 53)	39 (29, 47)	44 (35, 52)	47 (40, 55)	47 (40, 54)	＜0.001
Male, n (%)	0 (0%)	2,362,627 (69%)	240,465 (44%)	1,003,684 (65%)	580,348 (81%)	538,130 (90%)	＜0.001
BMI, kg/m2	583 (0.02%)	22.7 (20.5, 25.3)	20.6 (19.0, 22.4)	22.1 (20.2, 24.3)	23.9 (21.9, 26.4)	25.1 (23.1, 27.7)	＜0.001
BMI category, n (%)	583 (0.02%)						＜0.001
＜18.5		253,927 (7.4%)	94,257 (17%)	134,558 (8.7%)	18,893 (2.6%)	6,219 (1.0%)
≥ 18.5, ＜25		2,225,800 (65%)	409,169 (75%)	1,112,068 (72%)	424,860 (59%)	279,703 (47%)
≥ 25, ＜30		745,409 (22%)	37,387 (6.9%)	254,588 (16%)	213,376 (30%)	240,058 (40%)
≥ 30		189,803 (5.6%)	4,640 (0.9%)	52,494 (3.4%)	58,204 (8.1%)	74,465 (12%)
Waist circumference, cm	132,458 (3.9%)	81 (75, 88)	74 (69, 79)	79 (73, 86)	85 (79, 91)	88 (83, 94)	＜0.001
SBP, mmHg	601 (0.02%)	119 (109, 129)	112 (102, 122)	117 (107, 127)	122 (112, 132)	126 (116, 136)	＜0.001
DBP, mmHg	601 (0.02%)	74 (66, 82)	68 (61, 75)	72 (65, 80)	77 (69, 84)	80 (72, 87)	＜0.001
Hypertension, n (%)	106 (＜0.01%)	322,934 (9.5%)	14,951 (2.7%)	114,526 (7.4%)	94,118 (13%)	99,339 (17%)	＜0.001
Diabetes, n (%)	135 (＜0.01%)	101,163 (3.0%)	5,367 (1.0%)	34,569 (2.2%)	28,727 (4.0%)	32,500 (5.4%)	＜0.001
Cigarette smoking, n (%)	3,145 (0.1%)	962,386 (28%)	96,238 (18%)	396,624 (26%)	231,317 (32%)	238,207 (40%)	＜0.001
Alcohol consumption, n (%)	46,943 (1.4%)						＜0.001
Daily		841,859 (25%)	91,058 (17%)	361,431 (24%)	195,303 (28%)	194,067 (33%)
Sometimes		1,282,457 (38%)	213,951 (40%)	588,847 (38%)	264,246 (37%)	215,413 (36%)
None		1,244,263 (37%)	234,053 (43%)	582,703 (38%)	245,405 (35%)	182,102 (31%)
Glucose, mg/dL	131,318 (3.8%)	92 (86, 99)	88 (83, 93)	91 (86, 98)	94 (88, 102)	97 (90, 106)	＜0.001
HbA1c, %	360,965 (11%)	5.40 (5.20, 5.60)	5.30 (5.10, 5.50)	5.40 (5.20, 5.60)	5.50 (5.20, 5.70)	5.50 (5.30, 5.80)	＜0.001
LDL-C, mg/dL	0 (0%)	119 (99, 141)	99 (84, 116)	116 (98, 136)	131 (111, 152)	133 (111, 155)	＜0.001
HDL-C, mg/dL	0 (0%)	60 (51, 72)	72 (62, 82)	64 (55, 75)	55 (48, 64)	48 (42, 56)	＜0.001
TG, mg/dL	0 (0%)	84 (58, 127)	41 (36, 46)	71 (60, 84)	119 (109, 132)	199 (169, 256)	＜0.001
Statins, n (%)	0 (0%)	212,015 (6%)	8,095 (1.5%)	74,944 (4.8%)	65,460 (9.2%)	63,516 (11%)	＜0.001
Ezetimibe, n (%)	0 (0%)	22,022 (0.6%)	633 (0.1%)	6,362 (0.4%)	6,371 (0.9%)	8,656 (1.4%)	＜0.001
PCSK9 inhibitor, n (%)	0 (0%)	51 (＜0.1%)	3 (＜0.1%)	19 (＜0.1%)	16 (＜0.1%)	13 (＜0.1%)	0.037
Fibrate, n (%)	0 (0%)	44,468 (1.3%)	590 (0.1%)	8,507 (0.5%)	11,757 (1.6%)	23,614 (3.9%)	＜0.001
Omega-3 carboxylic acids, n (%)	0 (0%)	8,191 (0.2%)	242 (＜0.1%)	1,822 (0.1%)	2,090 (0.3%)	4,037 (0.7%)	＜0.001
EPA, n (%)	0 (0%)	13,944 (0.4%)	613 (0.1%)	3,899 (0.3%)	3,451 (0.5%)	5,981 (1.0%)	＜0.001

Data are n (%) or median (IQR).

BMI indicates Body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, hemoglobin A1c; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride, PCSK9, proprotein convertase subtilisin/kexin type9; EPA, eicosapentaenoic acid.

Supplementary Table 3.Clinical Outcomes of Primary Prevention Divided by the Four Triglyceride Category Groups

	Overall N=3,415,522	Very low (＜50 mg/dL) N=545,535	Low Normal (50 to 99 mg/dL) N=1,553,942	High Normal (100 to 149 mg/dL) N=715,470	Hypertriglyceridemia (≥ 150 mg/dL) N=600,575
Major adverse cardiovascular event	10,906 (0.3%)	469 (＜0.1%)	3,591 (0.2%)	3,076 (0.4%)	3,770 (0.6%)
Acute myocardial infarction	4,093 (0.1%)	98 (＜0.1%)	1,137 (＜0.1%)	1,204 (0.2%)	1,654 (0.3%)
Unstable angina	2,274 (＜0.1%)	90 (＜0.1%)	708 (＜0.1%)	669 (＜0.1%)	807 (0.1%)
Ischemic stroke	4,419 (0.1%)	250 (＜0.1%)	1,666 (0.1%)	1,183 (0.2%)	1,320 (0.2%)
All-cause death	5,033 (0.1%)	407 (＜0.1%)	2,096 (0.1%)	1,255 (0.2%)	1,275 (0.2%)

Data are n (%).

Supplementary Fig.2. Cumulative Incidence Rates of Clinical Outcomes in Primary Prevention Divided by the Four Triglyceride Category Groups

Line graphs show the incidence rates of (a) MACE, (b) AMI, (c) stroke, and (d) all-cause death in primary prevention according to the four TG category groups.

TG indicates triglyceride, MACE; major adverse cardiovascular event; AMI, acute myocardial infarction.

Fig.2. Association of Four Triglycerides Categories with Clinical Outcomes in Primary and Secondary Prevention

In the multivariable model, hazard ratios (HRs) were adjusted for age, sex, body mass index, smoking habits, hypertension, diabetes mellitus, LDL-C level, HDL-C level, and statin use. An iterative imputer method for missing values is performed with 10 iterations of Bayesian Ridge.

HR indicates hazard ratio. Other abbreviations are listed in Fig. 1.

Supplementary Table 4.Estimation for Hazard Risk and Population Attributable Fraction in Primary and Secondary Prevention Divided by the Four Triglyceride Category Groups

	Primary Prevention					Secondary Prevention
	No. of participants	No. of event	Incidence rate (95% CI) (per 100,000 person-year)	HR (95% CI)	PAF (%)	No. of participants	No. of event	Incidence rate (95% CI) (per 100,000 person-year)	HR (95% CI)	PAF (%)
MACE
Very Low (＜50 mg/dL)	545,535	469	29.2 (28.4, 29.9)	Ref	Ref	1,300	23	672.1 (599.8, 744.4)	Ref	NA
Low Normal (50 to 99)	1,553,942	3,591	71.8 (71.1, 72.6)	1.01 (0.91, 1.11)	5.48	11,092	244	784.9 (757.3, 812.6)	0.89 (0.58, 1.37)
High Normal (100 to 149)	715,470	3,076	127.5 (126.0, 129.0)	1.03 (0.93, 1.14)		8,790	225	868.1 (834.6, 901.5)	0.84 (0.54, 1.31)
Hypertriglyceridemia (≥ 150)	600,575	3,770	181.7 (179.7, 183.7)	1.15 (1.04, 1.28)		8,624	274	1062.4 (1024.8, 1100.0)	0.93 (0.60, 1.46)
AMI
Very Low (＜50 mg/dL)	545,535	98	6.1 (5.7, 6.4)	Ref	Ref	1,300	7	201.1 (161.2, 241.0)	Ref	NA
Low Normal (50 to 99)	1,553,942	1137	22.7 (22.3, 23.1)	1.16 (0.94, 1.43)	20.4	11,092	60	190.3 (176.6, 204.0)	0.65 (0.29, 1.43)
High Normal (100 to 149)	715,470	1,204	49.8 (48.9, 50.8)	1.24 (1.01, 1.54)		8,790	71	269.9 (251.1, 288.7)	0.73 (0.33, 1.62)
Hypertriglyceridemia (≥ 150)	600,575	1,654	79.5 (78.2, 80.9)	1.36 (1.10, 1.69)		8,624	89	339.3 (317.8, 360.7)	0.77 (0.34, 1.73)
Ischemic stroke
Very Low (＜50 mg/dL)	545,535	250	15.5 (15.0, 16.1)	Ref	Ref	1,300	3	86.4 (60.3, 112.6)	Ref	NA
Low Normal (50 to 99)	1,553,942	1,666	33.3 (32.8, 33.8)	1.10 (0.96, 1.27)	10.5	11,092	67	212.4 (198.0, 226.9)	2.05 (0.64, 6.55)
High Normal (100 to 149)	715,470	1,183	48.9 (48.0, 49.9)	1.08 (0.93, 1.25)		8,790	60	228.1 (210.8, 245.4)	2.01 (0.62, 6.53)
Hypertriglyceridemia (≥ 150)	600,575	1,320	63.4 (62.2, 64.6)	1.20 (1.04, 1.40)		8,624	77	293.4 (273.5, 313.3)	2.46 (0.75, 8.04)
All-cause death
Very Low (＜50 mg/dL)	545,535	407	25.3 (24.6, 26.0)	Ref	Ref	1,300	5	143.4 (109.7, 177.1)	Ref	NA
Low Normal (50 to 99)	1,553,942	2,096	41.8 (41.3, 42.4)	1.06 (0.95, 1.18)	NA	11,092	47	148.4 (136.3, 160.5)	0.89 (0.35, 2.28)
High Normal (100 to 149)	715,470	1,255	51.9 (50.9, 52.8)	1.04 (0.92, 1.17)		8,790	43	162.8 (148.2, 177.4)	0.92 (0.35, 2.40)
Hypertriglyceridemia (≥ 150)	600,575	1,275	61.2 (60.0, 62.4)	1.10 (0.97, 1.25)		8,624	42	159.1 (144.4, 173.9)	0.88 (0.33, 2.37)

HR was adjusted for age, sex, body mass index, smoking habit, hypertension, diabetes mellitus, LDL-C level, HDL-C level and statin use.

CI indicates confidence interval; ARD, absolute risk difference; HR, hazard ratio; PAF, population attributable fraction; MACE, major adverse cardiovascular event; AMI, acute myocardial infarction.

Subgroup Analysis in Primary Prevention

A subgroup analysis was conducted to assess whether the association of the TG category with clinical outcomes varied based on the current guideline risk category in primary prevention. The baseline characteristics divided by the risk category are summarized in Supplementary Table 5. As shown in Fig.3, the association between higher TG levels and higher risk of MACE and AMI within the range of more than 50mg/dL, compared with the very low TG group, was consistent among risk categories. The association between higher TG levels and higher risk of ischemic stroke within the range of more than 50mg/dL, compared with the very low TG group, was consistent in the low-risk and high-risk groups, however, that association was not observed in the intermediate risk group. In addition, the association between higher TG levels and higher risk of all-cause mortality was not observed among the risk categories (Fig.3). Interaction was observed between low-risk group and high-risk group in MACE, AMI, and ischemic stroke (p for interaction; ＜0.001, ＜0.001, and 0.002, respectively). Consistently, interaction was observed between low-risk group and intermediate-risk group in MACE, AMI, and ischemic stroke (p for interaction; ＜0.001, ＜0.001, and ＜0.001, respectively).

Supplementary Table 5.Baseline Characteristics Divided by the Risk Category in Primary Prevention

	Missing value, %	Overall, N= 3,415,522	High risk, N= 164,036	Intermediate risk, N= 957,626	Low risk, N= 2,293,860	p-value
Age, years	0%	44 (36, 53)	60 (53, 65)	54 (50, 59)	40 (32, 46)	＜0.001
Male, n (%)	0%	2,362,627 (69%)	150,733 (92%)	904,947 (94%)	1,306,947 (57%)	＜0.001
BMI, kg/m2	＜0.1%	22.7 (20.5, 25.3)	25.0 (22.8, 27.9)	24.0 (22.0, 26.4)	22.0 (20.0, 24.4)	＜0.001
SBP, mmHg	＜0.1%	119 (109, 129)	132 (121, 143)	128 (118, 138)	115 (106, 124)	＜0.001
DBP, mmHg	＜0.1%	74 (66, 82)	81 (74, 88)	81 (73, 88)	70 (64, 78)	＜0.001
Hypertension, n (%)	＜0.1%	322,934 (9.5%)	68,522 (42%)	179,561 (19%)	74,851 (3.3%)	＜0.001
Diabetes, n (%)	＜0.1%	101,163 (3.0%)	101,163 (62%)	0 (0%)	0 (0%)	＜0.001
Current smoking, n (%)	＜0.1%	962,386 (28%)	64,566 (39%)	382,530 (40%)	515,290 (22%)	＜0.001
Alcohol consumption, n (%)	1.4%					＜0.001
Everyday		841,859 (25%)	52,897 (33%)	348,675 (37%)	440,287 (19%)
Occasionally		1,282,457 (38%)	49,336 (31%)	314,951 (33%)	918,170 (41%)
Rarely		1,244,263 (37%)	59,517 (37%)	280,071 (30%)	904,675 (40%)
Glucose, mg/dL	3.8%	92 (86, 99)	119 (101, 143)	97 (90, 105)	90 (84, 96)	＜0.001
HbA1c, %	10.6%	5.40 (5.20, 5.60)	6.50 (5.80, 7.30)	5.50 (5.30, 5.80)	5.30 (5.10, 5.50)	＜0.001
LDL-C, mg/dL	0%	119 (99, 141)	124 (102, 147)	133 (112, 155)	113 (95, 133)	＜0.001
HDL-C, mg/dL	0%	60 (51, 72)	53 (45, 63)	56 (47, 67)	63 (53, 74)	＜0.001
TG, mg/dL	0%	84 (58, 127)	117 (82, 169)	110 (77, 161)	73 (52, 107)	＜0.001
Statin therapy, n (%)	0%	212,015 (6.2%)	49,785 (30%)	99,641 (10%)	62,589 (2.7%)	＜0.001
Regular		23,477 (0.7%)	5,651 (3.4%)	11,492 (1.2%)	6,334 (0.3%)
High intensity		188,538 (5.5%)	44,134 (27%)	88,149 (9.2%)	56,255 (2.5%)
Ezetimibe, n (%)	0%	22,022 (0.6%)	5,612 (3.4%)	10,148 (1.1%)	6,262 (0.3%)	＜0.001
PCSK9 inhibitor, n (%)	0%	51 (＜0.1%)	5 (＜0.1%)	21 (＜0.1%)	25 (＜0.1%)	n/a
Fibrate, n (%)	0%	44,468 (1.3%)	12,440 (7.6%)	21,592 (2.3%)	10,436 (0.5%)	＜0.001
Omega-3 carboxylic acids, n (%)	0%	8,191 (0.2%)	1,778 (1.1%)	3,984 (0.4%)	2,429 (0.1%)	＜0.001
EPA, n (%)	0%	13,944 (0.4%)	3,288 (2.0%)	6,800 (0.7%)	3,856 (0.2%)	＜0.001

Data are n (%) or median (IQR).

BMI indicates Body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, hemoglobin A1c; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride, PCSK9, proprotein convertase subtilisin/kexin type9; EPA, eicosapentaenoic acid.

Fig.3. Subgroup Analysis of Risk for Clinical Outcomes According Risk Category in Primary Prevention

Risk category was based on the Japan Atherosclerosis Society Guidelines for Prevention of Atherosclerotic Cardiovascular Diseases 2022. Prediction model for the onset of atherosclerotic cardiovascular disease using the Hisayama score including the factors such as age, sex, systolic blood pressure, prediabetes, LDL-C, HDL-C, and smoking status, was employed. Hazard ratios were adjusted for statin use. Abbreviations are listed in Figs. 1 and 2.

Another subgroup analysis stratified by the use of statins was performed. The baseline characteristics divided by the use of statins are summarized in Supplementary Table 6. As shown in Supplementary Fig.3, compared with the very low TG group, hypertriglyceridemia was associated with a higher risk of MACE, AMI, and ischemic stroke, and the high normal TG group was associated with a higher risk of AMI in the no use of statins group. However, in the statin use group, higher TG levels were not associated with higher risk of clinical outcomes. Interaction was observed between statins use and no use of statins in MACE, AMI, and ischemic stroke incidence (p for interaction; ＜0.001, ＜0.001, and ＜0.001, respectively).

Supplementary Table 6.Baseline Characteristics of Primary Prevention Divided by the use of statins

	Missing value, %	Overall, N= 3,415,522	No statin, N= 3,203,507	Statin, N= 212,015	p-value
Age, years	0%	44 (36, 53)	44 (35, 52)	55 (49, 61)	＜0.001
Male, n (%)	0%	2,362,627 (69%)	2,194,342 (68%)	168,285 (79%)	＜0.001
BMI, kg/m2	＜0.1%	22.7 (20.5, 25.3)	22.6 (20.4, 25.1)	24.9 (22.7, 27.6)	＜0.001
SBP, mmHg	＜0.1%	119 (109, 129)	119 (108, 129)	126 (116, 136)	＜0.001
DBP, mmHg	＜0.1%	74 (66, 82)	73 (66, 82)	79 (72, 86)	＜0.001
Hypertension, n (%)	＜0.1%	322,934 (9.5%)	234,164 (7.3%)	88,770 (42%)	＜0.001
Diabetes, n (%)	＜0.1%	101,163 (3.0%)	60,582 (1.9%)	40,581 (19%)	＜0.001
Current smoking, n (%)	＜0.1%	962,386 (28%)	908,947 (28%)	53,439 (25%)	＜0.001
Alcohol consumption, n (%)	1.4%				＜0.001
Everyday		841,859 (25%)	789,157 (25%)	52,702 (25%)
Occasionally		1,282,457 (38%)	1,210,491 (38%)	71,966 (34%)
Rarely		1,244,263 (37%)	1,160,111 (37%)	84,152 (40%)
Glucose, mg/dL	3.8%	92 (86, 99)	92 (86, 99)	100 (92, 114)	＜0.001
HbA1c, %	10.6%	5.40 (5.20, 5.60)	5.40 (5.20, 5.60)	5.80 (5.50, 6.20)	＜0.001
LDL-C, mg/dL	0%	119 (99, 141)	119 (99, 141)	116 (98, 138)	＜0.001
HDL-C, mg/dL	0%	60 (51, 72)	61 (51, 72)	56 (48, 66)	＜0.001
TG, mg/dL	0%	84 (58, 127)	82 (57, 124)	114 (82, 162)	＜0.001
Statin therapy, n (%)	0%	212,015 (6.2%)	0 (0%)	212,015 (100%)	＜0.001
regular		23,477 (0.7%)	0 (0%)	23,477 (11%)
strong		188,538 (5.5%)	0 (0%)	188,538 (89%)
Ezetimibe, n (%)	0%	22,022 (0.6%)	9,886 (0.3%)	12,136 (5.7%)	＜0.001
PCSK9 inhibitor, n (%)	0%	51 (＜0.1%)	7 (＜0.1%)	44 (＜0.1%)	＜0.001
Fibrate, n (%)	0%	44,468 (1.3%)	33,072 (1.0%)	11,396 (5.4%)	＜0.001
Omega-3 carboxylic acids, n (%)	0%	8,191 (0.2%)	4,317 (0.1%)	3,874 (1.8%)	＜0.001
EPA, n (%)	0%	13,944 (0.4%)	7,424 (0.2%)	6,520 (3.1%)	＜0.001

Data are n (%) or median (IQR).

BMI indicates Body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, hemoglobin A1c; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride, PCSK9, proprotein convertase subtilisin/kexin type9; EPA, eicosapentaenoic acid.

Supplementary Fig.3. Subgroup Analysis of Risk for Clinical Outcomes According Statin Therapy in Primary Prevention

The hazard ratios (HRs) were adjusted for age, sex, body mass index, smoking habits, hypertension, diabetes mellitus, LDL-C level, and HDL-C level. Abbreviations are listed in Supplementary Figures 1 and 2.

Discussion

Our analysis of a nationwide claims database yielded several key findings: 1) hypertriglyceridemia (TG ≥ 150 mg/dL) was associated with high risk of atherosclerotic cardiovascular events in the primary prevention cohort, but such association was not observed in the secondary prevention cohort. 2) Within a specific range of TG levels, an interaction was confirmed between categories of prevention and the association of TG levels with MACE (TG ＜150 mg/dL) and ischemic stroke (TG ≥ 150 mg/dL). 3) The PAFs of hypertriglyceridemia in primary prevention were significant, but in secondary prevention, due to non-significant hazard ratios, PAFs were not reported. 4) Adjusted for LDL-C, HDL-C, and statin use, lower risks of AMI were observed in the lower TG category than in the current threshold (i.e., 150 mg/dL at fasting) in primary prevention, but not in secondary prevention. 5) In participants with the high-risk group for primary prevention and undergoing statin therapy, higher TG levels were associated with a non-significant or modest increase in the risk of MACE. Conversely, in participants with low-risk and not receiving statin therapy, higher TG levels were associated with a significantly elevated risk of MACE. These findings highlight the need for tailored TG control strategies based on the prevention and risk category.

Previous studies indicate that elevated TG levels increase the risk of cardiovascular and cerebrovascular diseases, not only in Western countries but also in Asian regions^{11, 13, 15, 33-38)}. The SUITA study was a 15.1-year prospective cohort study involving 6,684 Japanese participants aged 30 to 79 years without a history of cardiovascular disease. It found that fasting triglyceride levels were an independent predictor of ischemic cardiovascular disease risk, even after adjusting for LDL-C levels. The association was more evident in individuals with LDL-C below 140 mg/dL¹⁵⁾. The CIRCS study, which involved 10,851 non-fasting and 4,057 fasting samples from Japanese residents aged 40 to 69 years, identified optimal cut-off points for triglyceride levels to predict ischemic heart disease. These were 145 mg/dL for non-fasting TG and 110 mg/dL for fasting TG, both lower than the recommended cut-off points in the US and Europe³⁸⁾. In alignment with these epidemiological studies, our study results revealed that elevated TG levels were significantly associated with an increased risk of atherosclerotic diseases, such as MACE, AMI, and ischemic stroke in primary prevention cohort (Fig.1, Table 3). Consequently, these segments may be potential candidates for cardioprotective interventions and pharmacotherapies against the high risk of atherosclerotic events. LDL-C-lowering therapy is well–established for the prevention of cardiovascular events. For secondary prevention, a combination of high-dose statins with agents such as ezetimibe and PCSK9 inhibitors is recommended^{4-8, 19, 39)}. Conversely, the effectiveness of pharmacotherapeutic interventions targeting TGs remains controversial. The JELIS study, a RCT involving 18,645 hypercholesterolemic patients, assessed the effects of EPA on MACE. Over a mean follow-up of 4.6 years, the EPA treatment group had a 19% relative reduction in the risk of MACE in Japanese primary and secondary prevention cohorts¹⁷⁾. Another multicenter, randomized, double-blind, placebo-controlled trial (REDUCE-IT trial) with 8,179 patients with elevated TG levels (70.7% for secondary prevention of cardiovascular events) investigated the effects of icosapent ethyl on ischemic events, resulting in a median follow-up of 4.9 years. Icosapent ethyl reduced the risk of the primary composite endpoint of cardiovascular events with an HR of 0.75 (95% CI, 0.68–0.83)¹⁸⁾. However, in a multinational double-blind RCT (PROMINENT trial) involving 10,497 patients with type 2 diabetes and mild to moderate hypertriglyceridemia (66.9% with previous cardiovascular disease), the effects of pemafibrate were neutral¹⁶⁾. After a median follow-up of 3.4 years, pemafibrate significantly reduced TGs by 26.2%, however, it did not significantly reduce the primary composite endpoint of cardiovascular events with an HR of 1.03 (95% CI, 0.91–1.15)¹⁶⁾.

The variations in the outcomes of these RCTs may stem largely from the different drugs employed in the interventions. However, the participants enrolled in these RCTs might not have been the most suitable candidates for TG interventions. What are the characteristics of candidates who require TG interventions, or what constitutes the optimal target values for management? Here, the association between TG levels and MACE risk in the secondary prevention cohort was not as strong as that in the primary prevention cohort, as shown in Fig.1 and Table 3. Additionally, hypertriglyceridemia was associated with a non-significant or modest increase in the risk of MACE in patients with the high-risk primary prevention and receiving statins (Fig.3 and Supplementary Fig.3). Conversely, in participants with the low-risk primary prevention and not receiving statin therapy, hypertriglyceridemia was associated with a significantly elevated risk of MACE (Fig.3 and Supplementary Fig.3). Based on these findings, the most suitable candidates for TG interventions are individuals with low-risk for primary prevention and not receiving statin therapy, but not those with high-risk for primary prevention, secondary prevention, or receiving statin therapy. Previous most RCTs comparing TG interventions focused on high-risk for primary prevention or secondary prevention with statin therapy, so the effects of TG interventions might be neutral. Therefore, TG intervention may be particularly beneficial for low-risk participants in primary prevention, though further RCT are needed to confirm its effectiveness.

Previous reports have shown that in patients receiving secondary prevention on statin therapy for LDL-C management, TG levels ＞200 mg/dL was associated with an increased presence of coronary artery plaques⁴⁰⁾. Furthermore, the intravascular ultrasound study did not reveal plaque regression effects at TG levels ＜150 mg/dL⁴⁰⁾. In this study, the HR for MACE and AMI in secondary prevention remained unchanged even when TG levels were below 150 mg/dL, aligning with prior findings. Although high MACE incidence in the secondary prevention cohort suggests a need for residual risk intervention, our results suggest that the primary prevention cohort might be more suitable for TG interventions. Additionally, in the primary prevention cohort, the PAFs for clinical outcomes associated with TG levels ≥ 50 mg/dL were higher than those associated with TG levels ≥ 150 mg/dL, both compared with their respective very low TG groups (＜50 mg/dL and ＜150 mg/dL, respectively) (Table 3 and Supplementary Table 4). This finding suggests that a stricter management target for TG levels may be warranted to achieve better clinical outcomes. The CIRCS study further supports this, identifying 110 mg/dL as the optimal fasting TG cut-off for predicting ischemic heart disease in a Japanese population, which is lower than Western recommendations³⁸⁾. Although the incidence rate of MACE was lower in the primary prevention cohort, this cohort represents a larger population. Therefore, early intervention with a lower TG target (TG ＜50 mg/dL) in primary prevention could reduce both disease and economic burdens. Future clinical studies should explore the benefits of targeting lower TG levels in individuals without a history of cardiovascular or cerebrovascular events.

The differential impact of TG levels on the risk of atherosclerotic cardiovascular events between the primary and secondary prevention cohorts warrants further consideration into the underlying pathophysiological mechanisms. It is imperative to consider the pathophysiological processes that may account for this variance in risk potency. While the current study has shed light on the association between TG levels and cardiovascular risk, it also unveils the complexity inherent in cardiovascular disease prevention, where one-size-fits-all approaches may not be adequate. Specifically, the primary prevention group, characterized by the absence of established cardiovascular disease, likely represents a more heterogeneous population in terms of atherosclerotic burden and metabolic profiles. Conversely, the secondary prevention group, already diagnosed with cardiovascular disease, may have a higher baseline risk, thus diminishing the relative contribution of TG as an independent risk factor. Moreover, statin therapy, commonly prescribed for secondary prevention, could modulate the influence of TGs on cardiovascular outcomes. This modulation might be due to the pleiotropic effects of statins, which extend beyond LDL-C lowering to involve anti-inflammatory and plaque-stabilizing actions, potentially overshadowing the impact of TG levels^41-43). This is because TGs contribute to atherosclerosis by macrophage uptake of triglyceride-rich lipoprotein remnants, promoting foam cell formation, lipoprotein lipase-mediated hydrolysis releasing oxidized fatty acids, inducing oxidative stress, inflammation, LDL oxidation, leukocyte adhesion, platelet aggregation, and coagulation⁴⁴⁾. Thus, these mechanisms of TG-induced atherosclerosis formation might be different between those with no prior cardiovascular disease and those with pre-existing conditions who take statins. This difference could contribute to the variations in risk intensity observed in the present study. However, further research is needed to fully understand the underlying mechanisms and confirm these findings.

This study had several limitations. First, its retrospective nature, relying on the JMDC Claims Database, may have introduced a selection bias. The JMDC database primarily consists of data from employer-provided health insurance plans, which over-represent working-age male participants, particularly in the secondary prevention cohort. As a result, there was a significant gender imbalance in our study population, with 92% of the secondary prevention cohort being male. This may limit the generalizability of our findings, particularly to female patients, and caution should be exercised when extrapolating these results to a more balanced gender distribution. Secondly, although we adjusted for numerous confounding factors, unmeasured confounders may have influenced the observed associations. Third, our findings were based mainly on the Japanese population, which may limit their generalizability to other ethnic groups or populations. Fourth, potential underreporting or misclassification of outcomes may have occurred, given the reliance on ICD-10 codes for event identification.

Conclusion

Our findings highlight the importance of managing TG levels, particularly in individuals without a history of cardiovascular or cerebrovascular events. The findings presented here may serve as a clarification call for clinicians, policymakers, and patients, highlighting the need for targeted management of TG levels based on the prevention and risk category.

Ethical Approval

The study protocol was approved by the Institutional Review Board of Kumamoto University Hospital (Rinri No. 2666) and was conducted in accordance with the principles outlined in the Declaration of Helsinki.

Data Availability Statement

Dr. Ishii received full access to all data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis.

Acknowledgments

We would like to acknowledge the assistance of ChatGPT-4 for English language editing of this manuscript, and we thank Editage (www.editage.com) for their English language editing services.

Sources of Funding

This work was supported by a grant for medical research from the Research Institute of Healthcare Data Science. However, it played no role in the design of the trial, collection or analysis of the data, interpretation of the trial results, or the writing of the manuscript.

Disclosures

K.T. received research grants from PPD-Shin Nippon Biomedical Laboratories and Alexion Pharmaceuticals and scholarship funds from Abbott Medical, Bayer, Boehringer Ingelheim, Daiichi Sankyo, ITI, Ono Pharmaceutical, Otsuka Pharmaceutical, and Takeda Pharmaceutical; affiliation with the endowed department from Abbott Medical, Boston Scientific, Cardinal Health, Fides-ONE, Fukuda Denshi, GM Medical, ITI, Japan Lifeline, Kaneka Medix, Medical Appliance, Medtronic, Nipro, and Terumo; and honoraria from Abbott Medical, Amgen, AstraZeneca, Bayer, Daiichi Sankyo, Medtronic, Kowa, Novartis Pharma, Otsuka Pharmaceutical, Pfizer, and Janssen Pharmaceutical. Other authors have no conflicting interests to disclose.

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