Low-Density-Lipoprotein Cholesterol Control and Treatment Status 1 Year after the Initial Health Checkup in Individuals with Referral-Level LDL Cholesterol

Abstract

Aims: Despite strong recommendations for medical consultation, the treatment status and low-density lipoprotein cholesterol (LDL-C) levels at 1-year follow-up of individuals with referral-level LDL-C identified in health checkups remain unclear. We evaluated the treatment status and 1-year LDL-C control among individuals identified in health checkups as requiring early medical consultation due to LDL-C levels of ≥ 180 mg/dL.

Methods: We conducted a nationwide cohort study including health checkup data for individuals aged 20–74 years. We identified 102,049 individuals (median age: 48 years; male: 66.8%) with uncontrolled LDL-C (≥ 180 mg/dL) at baseline, who had no prior lipid-lowering therapy. Poisson regression with robust error variance was used to assess factors associated with uncontrolled LDL-C at 1 year.

Results: Among individuals with LDL-C ≥ 180 mg/dL at baseline, 56,147 (55.0%) visited a medical institution within 3 months of the checkup, and 13,124 (12.9%) were prescribed lipid-lowering medications at 1 year. At 1 year follow-up, 49,260 (48.3%) still had LDL-C ≥ 180 mg/dL. Factors associated with persistent LDL-C ≥ 180 mg/dL at 1 year included obesity (RR: 1.07, [95% CI: 1.06–1.09]), 10 mg/dL increase in LDL-C at baseline (1.11 [1.10–1.11]), smoking (1.05 [1.04–1.07]), alcohol consumption (0.95 [0.94–0.97]), poor sleep quality (1.02 [1.01–1.03]), and skipping breakfast ≥ 3 times per week (1.07 [1.05–1.08]).

Conclusions: Despite being classified as requiring early medical intervention, only half of individuals with LDL-C ≥ 180 mg/dL visited a physician within 3 months, and nearly half continued to have uncontrolled LDL-C at 1 year. Strategies to facilitate timely medical visits and appropriate lipid management in health checkup-identified cases are warranted.

Abbreviations: ASCVD; atherosclerotic cardiovascular disease, BMI; body mass index, BP; blood pressure, CVD; cardiovascular disease, DBP; diastolic blood pressure, ICD-10; International Classification of Diseases; 10th Revision, SBP; systolic blood pressure

Introduction

Low-density lipoprotein cholesterol (LDL-C) is a well-established risk factor for cardiovascular disease (CVD) and remains a major contributor to global morbidity and mortality^{1, 2)}. Lowering LDL-C levels is a key strategy in preventing atherosclerotic cardiovascular disease (ASCVD), particularly in the context of primary prevention, where early identification and management of high-risk individuals is essential³⁾. In Japan, the Specific Health Checkup system was introduced to identify high-risk individuals and provide early intervention, particularly for those with LDL-C ≥ 180 mg/dL. This threshold is based on national guidelines and reflects a level that may also warrant evaluation for familial hypercholesterolemia (FH), a genetic disorder associated with markedly elevated cardiovascular risk^{4, 5)}. However, the extent to which individuals follow recommendations for medical consultation and treatment remains unclear⁶⁾.

Previous studies have demonstrated that timely initiation with lipid-lowering therapy, particularly statins, significantly reduces the risk of cardiovascular events⁷⁾. Despite these benefits, many patients do not receive appropriate therapy or fail to maintain adequate LDL-C control, which may be influenced by physicians’ prescribing habits, patient adherence, and socioeconomic factors⁸⁾. Furthermore, lifestyle modifications, including dietary adjustments, increased physical activity, and smoking cessation, play an essential role in LDL-C management but are often underutilized⁷⁾.

This study aims to assess the treatment status and LDL-C control among individuals identified through health checkups as requiring medical intervention. By evaluating medical visits, initiation of lipid-lowering therapy, and LDL-C control at 1 year, this study seeks to identify potential gaps in care and areas for improvement in primary prevention of cardiovascular disease.

Methods

Study Design and Data Source

This retrospective cohort study utilized the JMDC Claims Database, which contains comprehensive health checkup and insurance claims data for insured employees across Japan^9-11). Data were collected from January 2005 to April 2022 and included individuals aged 20–74 years who underwent annual health checkups and had LDL-C levels ≥ 180 mg/dL at baseline. The JMDC contracts with more than 60 insurers. Most insured individuals in the JMDC database are employees of relatively large Japanese companies. The JMDC Claims Database includes annual health checkup data, demographics, medical history, medications, and hospital claims with International Classification of Diseases, 10th Revision (ICD-10) coding.

Among the 5,127,304 individuals enrolled in the JMDC Claims Database, the current study included individuals aged 20–74 years with LDL-C levels ≥ 180 mg/dL at the initial health checkup and with LDL-C data available 1 year later. Exclusion criteria for the current study were as follows: (1) those aged < 20 years (n = 9,600), (2) those with LDL-C < 180 mg/dL at the initial health checkup (n = 4,918,519), (3) those with LDL-C data not available 1 year later (n = 66,555), (4) those with prior prescription for lipid-lowering medications (n = 5,827), (5) those with a history of myocardial infarction, angina pectoris, stroke, heart failure, dialysis, or kidney transplantation (to limit the study population to primary prevention) (n = 3,417), (6) those who developed myocardial infarction, angina pectoris, stroke, heart failure, dialysis, or kidney transplantation between the initial health checkup and 1 year later (n = 1,892), and (7) those with missing data on cigarette smoking (n = 5,436), alcohol consumption (n = 7,960), physical activity (n = 4,116), sleeping quality (n = 1,619), and skipping breakfast ≥ 3 times per week (n = 314). Consequently, the current study included 102,049 participants (Fig.1).

Fig.1. Flowchart

We extracted individuals aged 20–74 years with LDL-C levels ≥ 180 mg/dL at the initial health checkup and with LDL-C data available 1 year later in the JMDC Claims Database. We excluded participants if met the following reasons: (1) those aged < 20 years (n = 9,600), (2) those with LDL-C < 180 mg/dL at the initial health checkup (n = 4,918,519), (3) those with LDL-C data not available 1 year later (n = 66,555), (4) those with prior lipid-lowering medication prescriptions (n = 5,827), (5) those with a history of cardiovascular disease (CVD), dialysis, or kidney transplantation (n = 3,417), (6) those who developed CVD, dialysis, or kidney transplantation between the initial health checkup and 1 year later (n = 1,892), and (7) those with missing data on cigarette smoking (n = 5,436), alcohol consumption (n = 7,960), physical activity (n = 4,116), sleeping quality (n = 1,619), and skipping breakfast ≥ 3 times per week (n = 314). Consequently, the current study included 102,049 participants.

CVD; cardiovascular disease

Definitions

Obesity was defined as body mass index (BMI) ≥ 25 kg/m². Diabetes was defined as fasting glucose ≥ 126 mg/dL or use of glucose-lowering medications. Hypertension was defined as systolic blood pressure (SBP) ≥ 140 mmHg or diastolic blood pressure (DBP) ≥ 90 mmHg, or use of antihypertensive medications.

Information on cigarette smoking (current or noncurrent/never), alcohol consumption (daily or not daily), physical activity (active or inactive), restfulness from sleep (good or poor), and dietary habits (skipping breakfast ≥ 3 times per week: yes or no) was collected from a standardized self-reported questionnaire during the health checkup. We obtained data on medication prescriptions for lipid-lowering therapy (WHO-ATC codes starting with C10), antihypertensive therapy (WHO-ATC codes starting with C02, C03, C04, C07, C08, or C09), and glucose-lowering therapy (WHO-ATC codes starting with A10) from the claims data. A physician visit within 3 months was defined as any documented outpatient visit at a medical facility occurring within 3 months after the initial health check-up. This definition included all outpatient visits regardless of their clinical purpose or department, such as visits for seasonal illnesses, follow-up of unrelated chronic diseases, or general consultations.

Ethics

This study was approved by the University of Tokyo Ethics Committee (approval by the Institutional Review Board of the University of Tokyo: 2018–10862) and was conducted according to the Declaration of Helsinki. Because all data in the JMDC Claims Database were de-identified, the requirement for informed consent was waived in the current study.

Statistical Analysis

Descriptive statistics are presented as the median (interquartile range (IQR)) for continuous variables and as the number (percentage) for categorical variables. Furthermore, age-stratified lipid-lowering medication prescription rates were calculated for individuals with LDL-C ≥ 180 mg/dL at baseline. Poisson regression with robust error variance was employed to evaluate associations between the persistence of high-LDL-C persistence (LDL-C ≥ 180 mg/dL) at 1 year and various demographic and lifestyle factors. The relative risk (RR) was calculated after adjusting for potential confounders, including each of baseline variables (Table 2). To further illustrate the distribution of LDL-C levels, histograms for 1 year follow-up LDL-C were generated (Supplementary Fig.1).

Table 2.Factors associated with having LDL-C ≥ 180 mg/dL at 1 year after the initial health checkup

	Overall (n = 102,049)
	RR (95% CI)	P-value
Age per 10 years young	0.99 (0.98-1.00)	0.029
Sex, men	0.99 (0.98-1.01)	0.442
Obesity	1.07 (1.06-1.09)	< 0.001
10 mg/dL increase in low-density lipoprotein cholesterol	1.11 (1.10-1.11)	< 0.001
Hypertension	0.95 (0.94-0.97)	< 0.001
Diabetes mellitus	0.86 (0.83-0.89)	< 0.001
Cigarette smoking	1.05 (1.04-1.07)	< 0.001
Alcohol consumption	0.95 (0.94-0.97)	< 0.001
Physical inactivity	1.00 (0.99-1.01)	0.734
Poor sleep quality	1.02 (1.01-1.03)	0.005
Skipping breakfast ≥ 3 times per week	1.07 (1.05-1.08)	< 0.001

P values were calculated by the poisson regression with robust error variance analyses. All variables are simultaneously included in the model. Abbreviations: RR, relative risk; CI, confidence interval.

Supplementary Fig.1.

This histogram illustrates the distribution of LDL-C levels at 1 year after the initial health checkup among the 102,049 participants who had baseline LDL-C levels ≥ 180 mg/dL and no prior lipid-lowering therapy. The x-axis represents LDL-C levels in 10 mg/dL increments, and the y-axis shows the frequency (number of individuals) within each range.

We performed six sensitivity analyses. First, we repeated the Poisson regression with robust error variance after additionally adjusting for the visit to a physician within 3 months after undergoing a health checkup (Supplementary Table 1). Second, we conducted Poisson regression with robust error variance to examine the associations between the absence of lipid-lowering medication use at 1 year and various demographic and lifestyle factors (Table 3). Third, we conducted subgroup analyses stratified by age category (≥ 50 and < 50 years) and sex to assess factors associated with the absence of lipid-lowering medication use at 1 year (Supplementary Tables 2 and 3). Fourth, we conducted stratified analyses to assess factors associated with LDL-C ≥ 180 mg/dL at 1 year, based on whether a medical consultation occurred within 3 months after the initial health checkup (Supplementary Table 4). Fifth, we performed a similar stratified analysis based on whether a medical consultation occurred within 1 year after the initial health checkup (Supplementary Table 5). Sixth, we conducted a sensitivity analysis by excluding individuals with a diagnosis of familial hypercholesterolemia (specific disease codes; ‘8845524’, ‘8845523’, ‘8831271’, ‘2720001’) based on claims data (Supplementary Table 6).

Supplementary Table 1.Factors associated with having LDL-C ≥ 180 mg/dL at 1 year after the initial health checkup adjusted for the visit to a physician within 3 months after undergoing a health checkup

	Overall (n = 102,049)
	RR (95% CI)	P-value
Age per 10 years young	0.99 (0.98-1.00)	< 0.001
Sex, men	0.97 (0.95-0.98)	< 0.001
Obesity	1.07 (1.05-1.08)	< 0.001
10 mg/dL increase in low-density lipoprotein cholesterol	1.11 (1.10-1.11)	< 0.001
Hypertension	0.96 (0.95-0.98)	< 0.001
Diabetes mellitus	0.87 (0.85-0.90)	< 0.001
Cigarette smoking	1.03 (1.02-1.05)	< 0.001
Alcohol consumption	0.95 (0.94-0.97)	< 0.001
Physical inactivity	1.00 (0.99-1.02)	0.553
Poor sleep quality	1.03 (1.01-1.04)	< 0.001
Skipping breakfast ≥ 3 times per week	1.05 (1.04-1.06)	< 0.001
No visit to a physician within 3 months	1.25 (1.23-1.26)	< 0.001

P values were calculated by the poisson regression with robust error variance analyses. All variables are simultaneously included in the model. Abbreviations: RR, relative risk; CI, confidence interval.

Table 3.Factors associated with not receiving lipid-lowering medications at 1 year after the initial checkup

	Overall (n = 102,049)
	RR (95% CI)	P-value
Age per 10 years young	1.04 (1.04-1.04)	< 0.001
Sex, men	1.02 (1.02-1.03)	< 0.001
Obesity	1.01 (1.01-1.02)	< 0.001
10 mg/dL increase in low-density lipoprotein cholesterol	0.98 (0.98-0.98)	< 0.001
Hypertension	0.95 (0.95-0.96)	< 0.001
Diabetes mellitus	0.90 (0.89-0.91)	< 0.001
Cigarette smoking	1.02 (1.01-1.02)	< 0.001
Alcohol consumption	1.01 (1.00-1.02)	0.001
Physical inactivity	1.00 (0.99-1.00)	0.117
Poor sleep quality	1.00 (0.99-1.00)	0.259
Skipping breakfast ≥ 3 times per week	1.03 (1.02-1.03)	< 0.001

P values were calculated by the poisson regression with robust error variance analyses. All variables are simultaneously included in the model. Abbreviations: RR, relative risk; CI, confidence interval.

Supplementary Table 2.Factors associated with not receiving lipid-lowering medications at 1 year after the initial health checkup according to age category

	Age < 50 years (n = 57,422)		Age ≥ 50 years (n = 44,627)
	RR (95% CI)	P-value	RR (95% CI)	P-value
Age per 10 years young	1.04 (1.03-1.04)	< 0.001	1.02 (1.01-1.03)	< 0.001
Sex, men	1.01 (1.00-1.01)	0.104	1.04 (1.03-1.05)	< 0.001
Obesity	1.01 (1.00-1.01)	0.003	1.02 (1.01-1.03)	< 0.001
10 mg/dL increase in low-density lipoprotein cholesterol	0.98 (0.98-0.98)	< 0.001	0.98 (0.97-0.98)	< 0.001
Hypertension	0.96 (0.96-0.97)	< 0.001	0.94 (0.93-0.95)	< 0.001
Diabetes mellitus	0.90 (0.88-0.92)	< 0.001	0.90 (0.88-0.92)	< 0.001
Cigarette smoking	1.02 (1.01-1.02)	< 0.001	1.02 (1.01-1.03)	0.001
Alcohol consumption	1.01 (1.01-1.02)	< 0.001	1.00 (0.99-1.01)	0.415
Physical inactivity	1.00 (0.99-1.00)	0.098	1.00 (0.99-1.01)	0.445
Poor sleep quality	1.00 (0.99-1.00)	0.304	1.00 (0.99-1.01)	0.744
Skipping breakfast ≥ 3 times per week	1.03 (1.02-1.03)	< 0.001	1.04 (1.03-1.05)	< 0.001

P values were calculated by the poisson regression with robust error variance analyses. All variables are simultaneously included in the model. Abbreviations: RR, relative risk; CI, confidence interval.

Supplementary Table 3.Factors associated with not receiving lipid-lowering medications at 1 year after the initial health checkup according to sex

	Men (n = 68,207)		Women (n = 33,842)
	RR (95% CI)	P-value	RR (95% CI)	P-value
Age per 10 years young	1.03 (1.03-1.04)	< 0.001	1.05 (1.04-1.05)	< 0.001
Obesity	1.01 (1.00-1.01)	0.001	1.02 (1.01-1.03)	< 0.001
10 mg/dL increase in low-density lipoprotein cholesterol	0.98 (0.98-0.98)	< 0.001	0.98 (0.97-0.98)	< 0.001
Hypertension	0.96 (0.95-0.97)	< 0.001	0.94 (0.93-0.95)	< 0.001
Diabetes mellitus	0.90 (0.89-0.92)	< 0.001	0.88 (0.85-0.91)	< 0.001
Cigarette smoking	1.02 (1.01-1.03)	< 0.001	1.01 (0.99-1.02)	0.296
Alcohol consumption	1.01 (1.00-1.01)	0.031	1.02 (1.00-1.04)	0.028
Physical inactivity	0.99 (0.99-1.00)	0.047	1.00 (0.99-1.01)	0.649
Poor sleep quality	1.00 (0.99-1.00)	0.737	0.99 (0.98-1.00)	0.158
Skipping breakfast ≥ 3 times per week	1.03 (1.02-1.04)	< 0.001	1.03 (1.02-1.04)	< 0.001

P values were calculated by the poisson regression with robust error variance analyses. All variables are simultaneously included in the model. Abbreviations: RR, relative risk; CI, confidence interval.

Supplementary Table 4.Factors associated with having LDL-C ≥ 180 mg/dL at 1 year, stratified by medical consultation status within three months after initial health checkup

	With consultation (n = 56,147)		Without consultation (n = 45,902)
	RR (95% CI)	P-value	RR (95% CI)	P-value
Age per 10 years young	1.01 (0.995-1.02)	0.277	0.97 (0.96-0.98)	< 0.001
Sex (Male)	0.98 (0.96-1.00)	0.027	0.96 (0.94-0.98)	< 0.001
Obesity	1.09 (1.07-1.11)	< 0.001	1.04 (1.02-1.06)	< 0.001
10 mg/dL increase in low-density lipoprotein cholesterol	1.10 (1.10-1.11)	< 0.001	1.11 (1.10-1.11)	< 0.001
Hypertension	0.92 (0.90-0.95)	< 0.001	1.01 (0.99-1.03)	0.541
Diabetes mellitus	0.79 (0.76-0.83)	< 0.001	0.97 (0.93-1.01)	0.106
Cigarette smoking	1.04 (1.02-1.06)	0.001	1.03 (1.01-1.05)	0.001
Alcohol consumption	0.96 (0.93-0.98)	0.001	0.95 (0.93-0.97)	< 0.001
Physical inactivity	0.98 (0.96-1.00)	0.047	1.02 (1.01-1.04)	0.004
Poor sleep quality	1.03 (1.01-1.05)	0.001	1.02 (1.00-1.04)	0.037
Skipping breakfast ≥ 3 times per week	1.07 (1.04-1.09)	< 0.001	1.04 (1.02-1.06)	< 0.001

P values were calculated by the poisson regression with robust error variance analyses. All variables are simultaneously included in the model. Abbreviations: RR, relative risk; CI, confidence interval.

Supplementary Table 5.Factors associated with having LDL-C ≥ 180 mg/dL at 1 year, stratified by medical consultation status within 1 year after initial health checkup

	With consultation (n = 83,280)		Without consultation (n = 18,769)
	RR (95% CI)	P-value	RR (95% CI)	P-value
Age per 10 years young	1.00 (0.99-1.01)	0.798	0.97 (0.96-0.98)	< 0.001
Sex (Male)	0.98 (0.96-1.00)	0.016	0.95 (0.92-0.97)	< 0.001
Obesity	1.08 (1.07-1.10)	< 0.001	1.02 (1.00-1.05)	0.050
10 mg/dL increase in low-density lipoprotein cholesterol	1.10 (1.10-1.11)	< 0.001	1.12 (1.11-1.12)	< 0.001
Hypertension	0.93 (0.91-0.95)	< 0.001	1.03 (1.00-1.06)	0.029
Diabetes mellitus	0.82 (0.79-0.85)	< 0.001	1.02 (0.97-1.07)	0.457
Cigarette smoking	1.04 (1.03-1.06)	< 0.001	1.02 (0.99-1.04)	0.191
Alcohol consumption	0.96 (0.94-0.98)	< 0.001	0.94 (0.91-0.97)	< 0.001
Physical inactivity	0.99 (0.98-1.01)	0.491	1.03 (1.01-1.06)	0.008
Poor sleep quality	1.02 (1.01-1.04)	0.001	1.02 (1.00-1.05)	0.058
Skipping breakfast ≥ 3 times per week	1.07 (1.05-1.08)	< 0.001	1.02 (0.99-1.04)	0.221

P values were calculated by the poisson regression with robust error variance analyses. All variables are simultaneously included in the model. Abbreviations: RR, relative risk; CI, confidence interval.

Supplementary Table 6.Factors Associated with having LDL-C ≥ 180 mg/dL at 1 year after the initial health checkup excluding Individuals with a Diagnosis of Familial Hypercholesterolemia

	Overall (n = 101,859)
	RR(95% CI)	P-value
Age per 10 years young	0.99 (0.99-1.00)	0.031
Sex, men	0.99 (0.98-1.01)	0.444
Obesity	1.07 (1.06-1.09)	< 0.001
10 mg/dL increase in low-density lipoprotein cholesterol	1.11 (1.10-1.11)	< 0.001
Hypertension	0.95 (0.94-0.97)	< 0.001
Diabetes mellitus	0.86 (0.83-0.89)	< 0.001
Cigarette smoking	1.05 (1.04-1.07)	< 0.001
Alcohol consumption	0.95 (0.94-0.97)	< 0.001
Physical inactivity	1.00 (0.99-1.02)	0.718
Poor sleep quality	1.02 (1.01-1.03)	0.005
Skipping breakfast ≥ 3 times per week	1.07 (1.05-1.08)	< 0.001

P values were calculated by the poisson regression with robust error variance analyses. All variables are simultaneously included in the model. Abbreviations: RR, relative risk; CI, confidence interval.

Results

A total of 102,049 individuals (66.8% men) were included in our analysis. The median (IQR) age was 48 (41-54) years; median (IQR) SBP was 123 (112-133) mmHg; median (IQR) DBP was 77 (70-85) mmHg; the median (IQR) BMI was 24.3 (22.2-26.9) kg/m²; and the median (IQR) LDL-C was 192 (185-204) mg/dL at baseline (Table 1). Among those who were recommended to visit a medical institution, 56,147 individuals (55.0%) consulted a physician within 3 months after undergoing a health checkup. Conversely, 18,769 individuals had no documented medical institution visits within 1 year after their initial health checkup. Moreover, only 13,124 individuals (12.9%) were prescribed lipid-lowering medications at 1 year. The 1 year lipid-lowering medication prescription rates for individuals with LDL-C ≥ 180 mg/dL, stratified by age, were 6.4% for those aged 20-40 years, 13.6% for 40-60 years, and 19.5% for 60-74 years. At 1 year after the initial health checkup, the median (IQR) LDL-C was 178 (158-197) mg/dL, and 49,260 individuals (48.3%) had LDL-C levels of 180 mg/dL or greater. The distribution of LDL-C levels at 1 year follow-up is shown in Supplementary Fig.1.

Table 1.Baseline characteristics and treatment status

Variables	Overall (n = 102,049)	LDL-C < 180 mg/dL at 1 year after the initial health checkup (n = 52,789)	LDL-C ≥ 180 mg/dL at 1 year after the initial health checkup (n = 49,260)
Age (years)	48 (41-54)	48 (41-55)	48 (41-54)
Sex, men, n (%)	68,207 (66.8)	35,362 (67.0)	32,845 (66.7)
Body mass index (kg/m2)	24.3 (22.2-26.9)	24.2 (22.1-26.7)	24.5 (22.3-27.0)
Obesity, n (%)	43,806 (42.9)	21,791 (41.3)	22,015 (44.7)
Systolic blood pressure (mmHg)	123 (112-133)	122 (112-133)	123 (113-133)
Diastolic blood pressure (mmHg)	77 (70-85)	77 (70-85)	77 (70-85)
Hypertension, n (%)	23,347 (22.9)	12,477 (23.6)	10,870 (22.1)
Diabetes mellitus, n (%)	5,364 (5.3)	3,026 (5.7)	2,338 (4.7)
Cigarette smoking, n (%)	29,004 (28.4)	14,315 (27.1)	14,689 (29.8)
Alcohol consumption, n (%)	19,193 (18.8)	10,349 (19.6)	8,844 (18.0)
Physical inactivity, n (%)	57,260 (56.1)	29,501 (55.9)	27,759 (56.4)
Poor sleep quality, n (%)	39,816 (39.0)	20,281 (38.4)	19,535 (39.7)
Skipping breakfast ≥ 3 times per week, n (%)	31,158 (30.5)	15,086 (28.6)	16,072 (32.6)
Fasting blood glucose (mg/dL)	95 (88-103)	95 (88-103)	95 (88-102)
Low-density lipoprotein cholesterol (mg/dL)	192 (185-204)	189 (183-198)	196 (187-210)
High-density lipoprotein cholesterol (mg/dL)	56 (48-66)	57 (49-67)	55 (47-65)
Triglycerides (mg/dL)	125 (91-173)	121 (89-168)	129 (94-178)
Visit to a physician within 3 months after undergoing a health checkup, n (%)	56,147 (55.0)	31,930 (60.5)	24,217 (49.2)
Lipid-lowering medications at 1 year after the initial health checkup, n (%)	13,124 (12.9)	12,059 (22.8)	1,065 (2.2)
Low-density lipoprotein cholesterol at 1 year after the initial health checkup (mg/dL)	178 (158-197)	159 (139-170)	198 (188-213)
High-density lipoprotein cholesterol at 1 year after the initial health checkup (mg/dL)	55 (47-66)	56 (47-66)	55 (47-65)
Triglycerides at 1 year after the initial health checkup (mg/dL)	123 (88-174)	115 (82-166)	131 (95-180)

Data are expressed as median (interquartile range) or number (percentage).

Factors associated with LDL-C ≥ 180 mg/dL at 1 year after the initial health checkup were identified as obesity (RR: 1.07, 95% CI: 1.06-1.09), a 10 mg/dL increase in LDL-C at baseline (RR: 1.11, 95% CI: 1.10-1.11), hypertension (RR:0.95, 95% CI: 0.94-0.97), diabetes mellitus (RR:0.86, 95% CI: 0.83-0.89), cigarette smoking (RR: 1.05, 95% CI: 1.04-1.07) , alcohol consumption (RR: 0.95, 95% CI: 0.94-0.97), poor sleep quality (RR: 1.02, 95% CI: 1.01-1.03), and skipping breakfast ≥ 3 times per week (RR: 1.07, 95% CI: 1.05-1.08) (Table 2).

In the sensitivity analysis adjusted for the visit to a medical institution within 3 months, the absence of a physician visit within 3 months after the health checkup was associated with an increased risk of LDL-C ≥ 180 mg/dL at 1 year (RR: 1.25, 95% CI: 1.23-1.26, Supplementary Table 1). Factors associated with not receiving lipid-lowering medications at 1 year after the initial health checkup were additionally identified as younger age per 10 years (RR: 1.04, 95% CI: 1.04-1.04) and male sex (RR: 1.02, 95% CI: 1.02-1.03) (Table 3). Consistent associations were observed across age categories and sex (Supplementary Tables 2 and 3).

Furthermore, the factors associated with LDL-C ≥ 180 mg/dL at 1 year were examined separately by medical consultation status within 3 months and within 1 year after the initial health checkup (Supplementary Tables 4 and 5, respectively). These stratified analyses revealed different patterns in factors associated with persistent high LDL-C depending on consultation status. Specifically, factors related to LDL-C control varied depending on whether a medical institution was visited. In the group that visited a medical institution (With consultation), the presence of comorbidities such as hypertension or diabetes tended to be associated with better LDL-C control (i.e., a lower risk of LDL-C ≥ 180 mg/dL at 1 year) (Supplementary Tables 4 and 5).

In contrast, among those who did not consult a physician, younger age was associated with a lower risk of persistent high LDL-C, and other characteristics showed weaker or inconsistent associations with LDL-C control (Supplementary Tables 4 and 5).

When comparing the results presented in Table 2 and Table 3, it is noteworthy that certain variables demonstrated statistically significant associations in opposite directions. Specifically, each 10 mg/dL increase in baseline LDL-C was positively associated with the risk of LDL-C ≥ 180 mg/dL at 1 year (Table 2), whereas it was inversely associated with the likelihood of not receiving lipid-lowering medications (Table 3). Likewise, alcohol consumption was negatively associated with LDL-C elevation (Table 2) but positively associated with the absence of pharmacological intervention (Table 3).

Moreover, in a sensitivity analysis excluding individuals with a diagnosis of familial hypercholesterolemia, factors associated with persistent high LDL-C remained consistent with our main findings (Supplementary Table 6).

Discussion

These findings raise serious public health concerns, given that the primary objective of specific health guidance is to facilitate the early detection and treatment of cardiovascular and lifestyle-related diseases. This study specifically targeted individuals without a history of cardiovascular disease, thereby focusing on primary prevention. Effective LDL-C management in this population is particularly important, given the opportunity to prevent first cardiovascular events through early intervention. Similar patterns of suboptimal LDL-C control have been observed in international settings, indicating that this issue is not unique to Japan^12-19). Our findings, showing that nearly half of individuals with referral-level LDL-C (≥ 180 mg/dL) continued to have uncontrolled levels at 1 year, reflect a global challenge in lipid management. In the US, for instance, data from NHANES (2005-2008) indicated that only a third of adults with high LDL-C achieved guideline-recommended control¹²⁾. Similarly, European EUROASPIRE surveys consistently report suboptimal risk factor control, including dyslipidemia, among high-risk patients¹⁹⁾. Challenges in reaching LDL-C targets are also prevalent in Asian countries like South Korea and Taiwan, even among high-risk populations^{15, 18)}. While Japan’s universal health insurance and the Specific Health Checkup system are designed for early intervention, our study highlights a persistent gap between identification and effective management. This suggests that beyond initial screening, factors such as patient adherence, follow-up medical visits, and healthcare provider communication are critical. Cultural attitudes, potentially favoring lifestyle modifications over prompt pharmacotherapy, might also contribute to the observed patterns here compared to other nations.

Among individuals identified as requiring early medical intervention, failure to seek timely medical consultation was the strongest determinant of persistently elevated LDL-C levels 1 year after a health checkup. In this study, we identified several baseline factors associated with inadequate LDL-C control at 1 year, including higher baseline LDL-C, obesity, and smoking. Among these, the absence of a medical consultation within 3 months after the health checkup showed the strongest association with persistently high LDL-C levels. Unlike other lifestyle-related conditions such as obesity or hypertension, lifestyle modification alone often has a limited effect on LDL-C reduction. Pharmacological interventions—particularly statins—are the cornerstone of effective LDL-C management. Thus, early medical consultation, which facilitates timely initiation of lipid-lowering therapy, is likely a key determinant of improved LDL-C control at 1 year^20-25).

Interestingly, the presence of comorbidities such as hypertension or diabetes was associated with better LDL-C control at 1 year. Although this may appear counterintuitive, it is plausible that individuals with these conditions have more frequent medical visits, which may provide more opportunities for physicians to initiate lipid-lowering therapy. This finding suggests that regular contact with healthcare providers may play a key role in effective LDL-C management.

In addition, LDL-C levels of ≥ 180 mg/dL may also indicate possible familial hypercholesterolemia (FH), a genetic disorder associated with substantially elevated cardiovascular risk. Identifying and appropriately treating such individuals is critical, as delayed diagnosis and treatment of FH can result in missed opportunities for effective risk reduction²⁶⁾. FH is a genetic disorder associated with severely elevated LDL-C and high cardiovascular risk. We conducted a sensitivity analysis excluding 190 individuals with an FH diagnosis (specific disease codes; ‘8845524’, ‘8845523’, ‘8831271’, ‘2720001’), confirming our main findings remained consistent. However, FH is often underdiagnosed globally, and many undiagnosed cases likely exist in Japan. Our claims database, limited to specific disease codes, lacks crucial information like genetic testing or detailed family history for definitive FH diagnosis, hindering a full evaluation of undiagnosed FH’s impact on our results. Future research should focus on enhancing FH detection and treatment pathways within Japan’s health checkup system.

These findings underscore a substantial gap between health screening recommendations and subsequent clinical actions regarding LDL-C control. Bridging this gap will require targeted efforts to promote timely medical consultations and appropriate pharmacological interventions, particularly for individuals with markedly elevated LDL-C levels identified during health checkups.

A major strength of this study is its use of a large-scale claims database in Japan, allowing for a real-world analysis of LDL-C management. This study provides valuable insights into the long-term outcomes of individuals with referral-level LDL-C, highlighting significant gaps between strong medical recommendations and actual treatment implementation. However, several limitations must be acknowledged. First, as an observational study, residual confounders may exist despite adjustments. Second, the database does not include socioeconomic factors, making it impossible to fully adjust for potential residual unmeasured confounders. Nevertheless, given that Japan provides universal health coverage, economic factors are less likely to significantly influence the decision-making process for initiating lipid-lowering therapy as compared with other countries with different healthcare systems. Third, potential selection bias may have arisen during participant inclusion and exclusion. Fourth, claims-based diagnoses may be less rigorously validated compared to those in prospective registries, leading to possible underreporting of lipid-lowering medication use. Some participants may have obtained medications outside of the health insurance system, such as supplementary treatments, or may not have adhered to prescribed therapies. Fifth, our definition of a physician visit within 3 months included any outpatient encounter, which may encompass visits unrelated to hypercholesterolemia (e.g., seasonal illnesses such as influenza, allergic conditions, or follow-up visits for other chronic diseases like hypertension or diabetes). Moreover, the database does not allow for distinguishing the clinical department or specific reason for each visit, such as whether it was explicitly for lipid management. This limitation may lead to an overestimation of lipid-related medical consultations following health checkups. Sixth, although several factors such as age and sex demonstrated statistically significant associations with LDL-C outcomes, the corresponding effect sizes were small, and confidence intervals were unusually narrow. These results should be interpreted with caution, as they may represent limited clinical significance despite statistical significance in a large sample size. Finally, to evaluate the long-term effects of LDL-C control on cardiovascular outcomes, securing extended follow-up data is essential. The clinical importance of temporal trends in LDL-C management is acknowledged. However, the number of enrollees in the JMDC database has increased over time, and changes in population characteristics are anticipated. Therefore, comparability between time periods may be limited, and we did not evaluate chronological trends. This remains an important area for future research.

Conclusion

Despite strong clinical guidelines, 48.3% of individuals with referral-level LDL-C (≥ 180 mg/dL) failed to achieve adequate cholesterol control 1 year after their health checkup, highlighting the need for more proactive interventions. The low rate of lipid-lowering therapy initiation and persistent high LDL-C levels emphasize the need for more proactive approaches to cardiovascular risk reduction. Future research should explore long-term patient outcomes and interventions that enhance adherence to lipid-lowering therapies.

Acknowledgements

The authors’ responsibilities were as follows—HK, KK, and YS designed the research; YS analyzed data; HA, HK, and YS wrote the manuscript; AO, AM, KF, NT, HM, TA, KH, KN, YF, KM, HY, and NT interpreted data; HA, HK, and YS revised the manuscript; AO, AM, KF, NT, HM, TA, KH, KN, YF, KM, HY, and NT critically revised the manuscript for important intellectual content; HK had primary responsibility for final content; and all authors: read and approved the final manuscript.

Data Availability

This database is available for anyone who purchases it from JMDC Inc. (https://www.jmdc.co.jp/en/).

Sources of Funding

This work was supported by a grant from the Ministry of Health, Labour and Welfare, Japan (21AA2007).

Conflicts of Interest

Conflict of interest Research funding and scholarship funds (KF, KN, NT) were received from Fujitsu, Medtronic Japan, Boston Scientific, Simplex Quatum, Japan Lifeline, Murata Foundation, BIOTRONIK JAPAN, UT heart Inc., AstraZeneca, Bayer, Boehringer Ingelheim Japan, Daiichi Sankyo, Eli Lilly Japan, Kowa, Mitsubishi Tanabe, MSD, Novartis, Novo Nordisk, Otsuka, Astellas, Fuji Yakuhin, Mochida, Novartis, Abbott Medical, Daiichi Sankyo Healthcare, Mitsubishi Tanabe, Teijin, Kyowa Kirin Co Ltd, and Bristol Myers Squibb Japan.

Ethics Approval and Consent to participate

Name of the ethics committee: the Clinical Research Review Board of The University of Tokyo [2018-10862].

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