Associations of Dietary Intake With Cardiovascular Diseases, Blood Pressure, and Lipid Profile in the Korean Population: An Updated Systematic Review and Meta-Analysis

Jeongseon Kim; Madhawa Gunathilake; Tung Hoang; Oh Yoen Kim

doi:10.12997/jla.2026.15.1.111

. 2025 Jun 30;15(1):111–137. doi: 10.12997/jla.2026.15.1.111

Associations of Dietary Intake With Cardiovascular Diseases, Blood Pressure, and Lipid Profile in the Korean Population: An Updated Systematic Review and Meta-Analysis

Jeongseon Kim ^1,^*,^✉, Madhawa Gunathilake ^1,^*, Tung Hoang ^1,², Oh Yoen Kim ^3,⁴

PMCID: PMC12875075 PMID: 41660529

Abstract

Objective

This systematic review and meta-analysis of observational studies aimed to update the evidence regarding the association between dietary factors and cardiovascular disease (CVD) and related outcomes in the Korean population.

Methods

In total, 151 studies were included: 62 from a previous study and 89 identified through an updated search in PubMed and Embase. A random-effects model was applied to analyze pooled relative risks (RRs) and their 95% confidence intervals (CIs) for the consumption of 19 food items, 5 macronutrients, 14 micronutrients, 18 dietary indices, and 2 dietary patterns.

Results

Overall, higher fruit intake was associated with a lower risk of elevated blood pressure (BP)/hypertension (RR, 0.74; 95% CI, 0.65-0.84) and elevated/high triglycerides (TG) (RR, 0.82; 95% CI, 0.71–0.95). Higher vegetable intake was associated with a lower risk of elevated/high TG (RR, 0.92; 95% CI, 0.87–0.97). Inverse associations were observed between higher milk and dairy consumption and elevated BP/hypertension (RR, 0.89; 95% CI, 0.83–0.95), elevated/high TG (RR, 0.82; 95% CI, 0.76–0.89), and lower high-density lipoprotein cholesterol (HDL-C) levels (RR, 0.82; 95% CI, 0.75–0.89). Coffee consumption was inversely associated with the risk of CVD (RR, 0.80; 95% CI, 0.67–0.95) and elevated/high TG (RR, 0.84; 95% CI, 0.79–0.89). Consumption of sugar-sweetened beverages was positively associated with an increased risk of elevated BP/hypertension (RR, 1.21; 95% CI, 1.09–1.33) and elevated/high TG (RR, 1.20; 95% CI, 1.03–1.41).

Conclusion

This study suggests that higher intake of fruits, vegetables, milk and dairy, and coffee may confer potential benefits for CVD and its associated risk factors, such as BP and lipid profiles. In contrast, sugar-sweetened beverages appear detrimental to cardiovascular health.

Keywords: Diet, Cardiovascular diseases, Hypertension, Dyslipidemia, Meta-analysis

INTRODUCTION

Cardiovascular disease (CVD) has remained the leading cause of non-communicable disease mortality globally over the past decade, significantly contributing to worldwide mortality and healthcare costs.¹,² Age-standardized CVD mortality rates vary greatly by region, ranging from 73.6 per 100,000 individuals in high-income Asia Pacific regions to 432.3 per 100,000 in Eastern Europe as of 2022.³ A study projecting the global burden of CVD from 2025 to 2050 estimated an expected 35.6 million cardiovascular deaths by 2050, with a projected decrease of 30.5% in age-standardized mortality.¹ In Korea, although the incidence and mortality rates of CVD have increased for decades, the age-standardized mortality rate has recently begun to decline.⁴

As a substantial proportion of CVD risk is attributable to common modifiable risk factors, trends in the incidence and predominant subtypes of CVD can be anticipated based on changes in cardiovascular risk factors.⁴,⁵,⁶ Advances in disease treatments, alongside lifestyle and nutritional modifications influencing CVD risk, have been closely associated with the declining trend in CVD mortality.⁷ It has been established that major risk factors such as high blood pressure (BP), hyperlipidemia, diabetes, and smoking significantly contribute to CVD among Asian populations, including Korea.⁴,⁸ A study assessing sex differences in CVD risk factors and quality of life among individuals with hypertension in Korea from 2013 to 2018 reported that specific risk profiles differ between men and women; women exhibited lower physical activity levels and higher prevalence of elevated cholesterol, while men displayed higher body mass indices and poorer dietary management.⁹ Additionally, a nationwide cohort study involving over 3.6 million young adults evaluated the relationship between cumulative exposure to metabolic risk factors and CVD, concluding that CVD risk increased in an exposure-dependent manner, with persistent exposure to elevated BP having the strongest association with increased risk.¹⁰

Healthy dietary patterns and lifestyle changes are considered the most effective methods for preventing CVD.¹¹ Diets rich in plant-based foods, in particular, are associated with reduced lipid levels, suggesting a potential protective effect against CVD.¹²,¹³ A recent meta-analysis reported that vegetarian diets are associated with a reduced risk of CVD and ischemic heart diseases, though not with stroke.¹⁴ Additionally, a dose-response meta-analysis of prospective studies examining the relationship between fruit and vegetable intake and CVD risk reported inverse associations.¹⁵ Another recent meta-analysis concluded that high-carbohydrate diets may elevate CVD risk, especially among Asian populations.¹⁶

The fifth guidelines for managing dyslipidemia, specifically addressing dietary management for Koreans, have recently been published.¹⁷ Since most recommendations were extrapolated from guidelines based on Western populations, we initiated this systematic review and meta-analysis to evaluate the current evidence regarding associations between dietary intake and the risk of CVD, hypertension, and dyslipidemia among Koreans.² This systematic review and meta-analysis aimed to update the evidence on dietary factors and their association with CVD and related outcomes in the Korean population.

MATERIALS AND METHODS

1. Data sources and literature search

In our initial review, we systematically searched the PubMed and Embase databases from their inception through December 12, 2019. For the updated review, we conducted a literature search from December 13, 2019, to February 1, 2024, following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines,¹⁸ and we plan to update this evidence at 5-year intervals. The search terms were based on the Population, Intervention, Comparison, and Outcome (PICO) guidelines,¹⁹ and included dietary intake and its synonyms as exposure terms (“diet,” “dietary,” “intake,” and “consumption”); cardiovascular disease, hypertension, and dyslipidemia as outcome terms (“cardiovascular disease,” “coronary heart disease,” “heart attack,” “myocardial infarction,” “cerebrovascular disease,” “stroke,” “peripheral vascular disease,” “heart failure,” “rheumatic heart disease,” “congenital heart disease,” “cardiomyopathy,” “hypertension,” “blood pressure,” “dyslipidemia,” “hyperlipidemia,” “cholesterol,” “high-density lipoprotein,” “low-density lipoprotein,” and “triglyceride”); and the Korean population (“Korea” and “Korean”). No limitations were placed on language or format (abstract or full-text), but conference abstracts lacking a published full-text version were excluded.

2. Inclusion and exclusion criteria

All studies evaluating the association between dietary intake and risk of CVD, hypertension, or dyslipidemia in the Korean population were eligible for inclusion in this updated meta-analysis. Inclusion criteria were as follows: studies conducted on the Korean population; dietary intake of any food items, dietary patterns, or dietary indices as exposure; outcomes including any type of CVD, elevated BP or hypertension, or abnormalities in any lipid markers, such as total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C); and the reporting of odds ratios (ORs) or hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) for associations between exposures and outcomes. When studies overlapped in terms of both exposure and outcome, the most comprehensive study—defined as the one with the largest sample size, longest follow-up period, and most complete data on relevant covariates—was selected for final analysis.

3. Data extraction

Two researchers (J.K. and M.G.) independently selected studies according to eligibility criteria, and any discrepancies were resolved through review by a third researcher (T.H.). The following information was extracted from the eligible studies: first author’s name, publication year, project name, study design, duration of participant recruitment, follow-up time (for prospective studies only), sample size, type of exposure, type of outcome, confounding variables, ORs/HRs with corresponding 95% CIs comparing the highest quantile of dietary consumption to the lowest quantile (from models adjusted for the greatest number of potential confounders), and dietary assessment methods such as 24-hour dietary recalls, food frequency questionnaires, or other approaches. For studies that used the highest consumption level as the reference group, ORs/HRs and 95% CIs were recalculated so that the lowest consumption level became the reference.

4. Quality assessment

The Newcastle-Ottawa Scale (NOS) was employed to assess the methodological quality of individual studies, including cross-sectional, case-control, and cohort studies.²⁰ Two investigators (J.K. and M.G.) independently scored each study based on the 3 NOS subscales: study selection, comparability, and exposure (or outcome) assessment. All discrepancies were reviewed and resolved by a third researcher (T.H.). Studies with NOS scores of 6 or higher were considered high quality.

5. Statistical analyses

When a pooled effect size was generated from 2 or more individual studies, heterogeneity across studies was evaluated using the I² statistic, with an I² value greater than 50% indicating substantial heterogeneity.²¹ Assuming variations in participant characteristics across studies, we applied a random-effects model using the DerSimonian and Laird method to derive pooled effect sizes.²² We conducted subgroup analyses based on study design and population characteristics. Publication bias was evaluated with the Begg funnel plot and Egger test²³,²⁴ when at least 5 studies were available. Leave-one-out sensitivity analyses were performed to examine the robustness of the meta-analysis results when publication bias was detected. All statistical analyses were conducted using Stata SE version 14.0 (StatCorp).

RESULTS

1. Study selection

The initial study selection process for our first publication, which selected 62 studies²⁵,²⁶,²⁷,²⁸,²⁹,³⁰,³¹,³²,³³,³⁴,³⁵,³⁶,³⁷,³⁸,³⁹,⁴⁰,⁴¹,⁴²,⁴³,⁴⁴,⁴⁵,⁴⁶,⁴⁷,⁴⁸,⁴⁹,⁵⁰,⁵¹,⁵²,⁵³,⁵⁴,⁵⁵,⁵⁶,⁵⁷,⁵⁸,⁵⁹,⁶⁰,⁶¹,⁶²,⁶³,⁶⁴,⁶⁵,⁶⁶,⁶⁷,⁶⁸,⁶⁹,⁷⁰,⁷¹,⁷²,⁷³,⁷⁴,⁷⁵,⁷⁶,⁷⁷,⁷⁸,⁷⁹,⁸⁰,⁸¹,⁸²,⁸³,⁸⁴,⁸⁵,⁸⁶ for meta-analysis, has been previously presented in a PRISMA flow chart.² In the current updated review, a total of 3,507 records were identified through electronic database searches and manual searches of bibliographies. After removing duplicates, the titles and abstracts of 2,850 articles were screened for relevance. Of these, 2,616 articles were excluded as irrelevant, leaving 234 articles for full-text assessment based on the predefined eligibility criteria. Subsequently, 145 additional articles were excluded for the following reasons: unrelated to exposure and/or outcome (n=89), methodological irrelevance (n=29), unrelated population (n=6), conference abstracts only (n=9), letters to the editor (n=2), no reported values (n=5), and overlapping with the previous review (n=5). Consequently, 89 new studies⁸⁷,⁸⁸,⁸⁹,⁹⁰,⁹¹,⁹²,⁹³,⁹⁴,⁹⁵,⁹⁶,⁹⁷,⁹⁸,⁹⁹,¹⁰⁰,¹⁰¹,¹⁰²,¹⁰³,¹⁰⁴,¹⁰⁵,¹⁰⁶,¹⁰⁷,¹⁰⁸,¹⁰⁹,¹¹⁰,¹¹¹,¹¹²,¹¹³,¹¹⁴,¹¹⁵,¹¹⁶,¹¹⁷,¹¹⁸,¹¹⁹,¹²⁰,¹²¹,¹²²,¹²³,¹²⁴,¹²⁵,¹²⁶,¹²⁷,¹²⁸,¹²⁹,¹³⁰,¹³¹,¹³²,¹³³,¹³⁴,¹³⁵,¹³⁶,¹³⁷,¹³⁸,¹³⁹,¹⁴⁰,¹⁴¹,¹⁴²,¹⁴³,¹⁴⁴,¹⁴⁵,¹⁴⁶,¹⁴⁷,¹⁴⁸,¹⁴⁹,¹⁵⁰,¹⁵¹,¹⁵²,¹⁵³,¹⁵⁴,¹⁵⁵,¹⁵⁶,¹⁵⁷,¹⁵⁸,¹⁵⁹,¹⁶⁰,¹⁶¹,¹⁶²,¹⁶³,¹⁶⁴,¹⁶⁵,¹⁶⁶,¹⁶⁷,¹⁶⁸,¹⁶⁹,¹⁷⁰,¹⁷¹,¹⁷²,¹⁷³,¹⁷⁴—comprising 59 cross-sectional, 1 case-control, and 29 cohort studies—were deemed eligible and had not been included in the prior meta-analysis. In total, this updated systematic review incorporated 151 studies (Fig. 1).

2. Study characteristics and quality assessment

Supplementary Table 1 presents the general characteristics of the included studies. Ninety-four articles (including 65 cross-sectional and case-control studies, and 29 cohort studies) provided ORs or HRs for single food items, macronutrients, and micronutrients. Additionally, 18 cross-sectional and 8 cohort studies reported ORs or HRs for dietary indices, and 15 cross-sectional and 6 cohort studies reported ORs or HRs for dietary patterns. Studies originated from various Korean cohorts: the Korean National Health and Nutrition Examination Survey (KNHANES) (n=53), the Ansan-Ansung or Urban cohort (n=9), the Korean Genome and Epidemiology Study (KoGES) cohort (n=12), the Health Examinee (HEXA) cohort (n=9), the Korea National Cancer Screenee Cohort (KNCC) (n=3), and National Sample cohorts, including hospital-based and/or community-based studies (n=3). Dietary assessment methods varied among the studies. The food frequency questionnaire (FFQ) was the most frequently utilized method (n=89), followed by the 24-hour dietary recall method (n=54). Three studies employed both FFQ and 24-hour dietary recall methods, 1 study combined the 24-hour dietary recall with a 3-day dietary record, and 3 studies used self-administered questionnaires.

Supplementary Tables 2, 3, 4 present quality assessments of individual studies using the NOS. All included studies were categorized as high-quality, with NOS scores of 6 or higher. Median quality scores were 8 (out of 10) for cross-sectional studies, 6 (out of 9) for case-control studies, and 8 (out of 9) for cohort studies. Most cross-sectional studies exhibited selection bias due to differences in characteristics between responders and non-responders. The case-control studies reported selection bias regarding control selection, unblinded dietary intake assessments, and exposure bias due to non-reporting of non-response rates. In cohort studies, outcome bias related to unblinded outcome assessment was noted.

3. Main analyses

Table 1 provides a comprehensive evaluation of associations between single food items, macronutrients, micronutrients, dietary indices, and outcomes including CVD, hypertension, and lipid profiles. Overall, higher fruit intake was associated with a lower risk of elevated BP/hypertension (relative risk [RR], 0.74; 95% CI, 0.65–0.84) and elevated/high TG (RR, 0.82; 95% CI, 0.71–0.95). Higher vegetable intake was associated with a reduced risk of elevated/high TG (RR, 0.92; 95% CI, 0.87–0.97). Meat consumption was associated with an increased risk of CVD (RR, 1.13; 95% CI, 1.04–1.22). Higher consumption of milk and dairy was inversely associated with elevated BP/hypertension (RR, 0.89; 95% CI, 0.83–0.95), elevated/high TG (RR, 0.82; 95% CI, 0.76–0.89), and lower HDL-C levels (RR, 0.82; 95% CI, 0.75–0.89). Coffee consumption was inversely associated with CVD risk (RR, 0.80; 95% CI, 0.67–0.95) and elevated/high TG (RR, 0.84; 95% CI, 0.79–0.89), with limited evidence indicating a potential reduction in elevated BP/hypertension risk (RR, 0.91; 95% CI, 0.83–1.00). Limited evidence also indicated that egg consumption was associated with a lower risk of elevated BP/hypertension (RR, 0.88; 95% CI, 0.78–0.99). Consumption of sugar-sweetened beverages was positively associated with an increased risk of elevated BP/hypertension (RR, 1.21; 95% CI, 1.09–1.33) and elevated/high TG (RR, 1.20; 95% CI, 1.03–1.41). Intake of colorful foods—including rice, beans, fruits, and vegetables—was inversely associated with elevated BP/hypertension (RR, 0.94; 95% CI, 0.93–0.98). Consumption of fast foods/oil/fat products was positively associated with elevated BP/hypertension risk (RR, 1.22; 95% CI, 1.13–1.32). Nut intake showed a limited but significant inverse association with elevated BP/hypertension (RR, 0.83; 95% CI, 0.76–0.90). Soy and/or fermented soy food intake was inversely associated with elevated BP/hypertension (RR, 0.86; 95% CI, 0.76–0.98). Tea consumption was negatively associated with elevated/high TG (RR, 0.86; 95% CI, 0.79–0.93).

Table 1. Meta analysis of the associations of food items, nutrients, and dietary indices with cardiovascular diseases, hypertension, and lipid profile.

Variable		CVD		Elevated BP/hypertension		Elevated/high TC		Elevated/high total TG		Low HDL-C		High LDL-C
Variable		n (I²)	OR/HR (95% CI)	n (I²)	OR/HR (95% CI)	n (I²)	OR/HR (95% CI)	n (I²)	OR/HR (95% CI)	n (I²)	OR/HR (95% CI)	n (I²)	OR/HR (95% CI)
Food items
	Fruits/fruit juice	4 (0.0%)	0.74 (0.53–1.03)	12 (89.3%)	0.74 (0.65–0.84)	1 (NA)	0.85 (0.34–2.13)	8 (89.0%)	0.82 (0.71–0.95)	8 (56.9%)	0.99 (0.93–1.06)	1 (NA)	1.08 (0.95–1.23)
	Vegetables	4 (57.6%)	0.89 (0.77–1.03)	13 (60.2%)	0.94 (0.88–1.01)	1 (NA)	0.65 (0.28–1.49)	8 (17.0%)	0.92 (0.87–0.97)	8 (73.1%)	0.96 (0.87–1.06)	2 (33.4%)	0.99 (0.88–1.10)
	Fish			1 (NA)	0.61 (0.38–0.98)	2 (4.1%)	1.02 (0.94–1.11)	3 (56.9%)	0.83 (0.66–1.04)	3 (46.6%)	0.89 (0.65–1.22)	2 (43.1%)	1.11 (0.95–1.31)
	Seafood	3 (62.9%)	0.91 (0.60–1.39)	2 (94.9%)	0.84 (0.36–1.96)			1 (NA)	0.67 (0.51–0.88)	1 (NA)	0.66 (0.48–0.93)
	Sugar	1 (NA)	4.57 (1.94–11.40)	3 (0.0%)	1.17 (1.07–1.28)	2 (0.0%)	1.24 (1.05–1.48)	3 (0.0%)	1.06 (0.98–1.16)	4 (35.0%)	1.02 (0.92–1.12)	2 (0.0%)	0.87 (0.74–1.03)
	Eggs	3 (0.0%)	1.08 (0.88–1.31)	5 (65.3%)	0.88 (0.78–0.99)	1 (NA)	1.08 (0.97–1.21)	2 (87.2%)	0.85 (0.71–1.03)	2 (34.4%	0.84 (0.78–0.91)	3 (0.0%)	1.06 (0.99–1.12)
	Meat	6 (64.2%)	1.13 (1.04–1.22)	1 (NA)	0.68 (0.56–0.83)	3 (59.6%)	1.17 (1.09–1.25)	2 (58.5%)	1.10 (0.95–1.27)	2 (0.0%)	0.95 (0.80–1.12)	5 (81.2%)	1.07 (1.01–1.14)
	Milk and dairy	3 (78.1%)	0.80 (0.57–1.11)	11 (74.6%)	0.89 (0.83–0.95)			8 (64.5%)	0.82 (0.76–0.89)	8 (74.8%)	0.82 (0.75–0.89)	2 (0.0%)	1.05 (1.03–1.07)
	Sugar-sweetened beverages	2 (18.5%)	1.26 (0.82–1.93)	6 (0.0%)	1.21 (1.09–1.33)			3 (0.0%)	1.20 (1.03–1.41)	3 (87.7%)	1.20 (0.80–1.80)
	Coffee	8 (72.1%)	0.80 (0.67–0.95)	11 (74.5%)	0.91 (0.83–1.00)			8 (11.0%)	0.84 (0.79–0.89)	8 (81.7%)	0.87 (0.76–0.99)
	Fried foods			1 (NA)	1.56 (1.14–2.15)			1 (NA)	1.22 (0.92–1.62)	1 (NA)	0.85 (0.66–1.10)
	Colorful foods (rice, beans, fruits, vegetables)			4 (1.5%)	0.94 (0.93–0.98)			4 (0.0%)	0.98 (0.94–1.03)	4 (58.5%)	1.05 (0.97–1.15)
	Fat-rich processed foods	1 (NA)	2.44 (1.05–5.70)	2 (0.0%)	1.22 (1.13–1.32)			1 (NA)	1.06 (0.96–1.18)	1 (NA)	0.92 (0.83–1.02)	2 (12.2%)	1.04 (1.01–1.08)
	Nuts			3 (0.0%)	0.83 (0.76–0.90)	1 (NA)	0.65 (0.30–1.40)					2 (0.0%)	0.92 (0.82–1.03)
	Soy/fermented soy foods	3 (59.8%)	0.69 (0.47–1.02)	3 (0.0%)	0.86 (0.76–0.98)			1 (NA)	0.93 (0.75–1.15)	1 (NA)	1.14 (0.98–1.34)
	Tea	3 (97.0%)	1.59 (0.66–3.80)	4 (97.5%)	1.56 (0.98–2.48)			2 (0.0%)	0.86 (0.79–0.93)	2 (0.0%)	0.83 (0.76–0.90)
	Curry-rice	4 (75.1%)	0.46 (0.35–0.60)	1 (NA)	0.53 (0.48–0.59)	1 (NA)	0.91 (0.86–0.98)	1 (NA)	0.76 (0.71–0.81)	1 (NA)	0.89 (0.84–0.96)	1 (NA)	0.94 (0.83–1.06)
	Dietary resistant starch			2 (0.0%)	0.79 (0.68–0.92)			2 (37.0%)	0.87 (0.72–1.06)	2 (0.0%)	0.93 (0.80–1.08)
	Noodle					2 (69.9%)	1.11 (0.96–1.29)	2 (0.0%)	1.23 (1.14–1.33)	2 (0.0%)	1.07 (0.98–1.15)	2 (0.0%)	1.14 (1.04–1.26)
Macronutrients
	Carbohydrates	3 (58.9%)	1.78 (1.09–2.88)	6 (25.7%)	1.03 (0.93–1.15)	5 (0.0%)	0.80 (0.70–0.91)	7 (0.0%)	1.21 (1.08–1.35)	5 (27.9%)	1.21 (1.07–1.37)	3 (15.9%)	0.89 (0.76–1.05)
	Protein	2 (87.6%)	0.94 (0.77–1.14)	1 (NA)	1.01 (0.67–1.53)	2 (85.0%)	0.87 (0.71–1.07)	7 (43.1%)	0.88 (0.78–0.99)	7 (66.0%)	0.94 (0.80–1.09)	2 (80.8%)	0.91 (0.71–1.18)
	Fat	2 (73.2%)	1.01 (0.86–1.19)	2 (0.0%)	0.86 (0.77–0.95)	4 (0.0%)	1.25 (1.14–1.37)	1 (NA)	0.94 (0.93–0.96)	3 (0.0%)	0.87 (0.80–0.95)	2 (0.0%)	1.34 (1.15–1.56)
	Carbohydrate + protein			2 (44.3%)	1.13 (0.62–2.07)	2 (0.0%)	1.75 (1.09–2.82)	2 (21.2%)	1.10 (0.68–1.81)	2 (85.9%)	1.56 (0.53–4.61)	2 (23.9%)	1.37 (0.75–2.50)
	Fiber			2 (0.0%)	0.91 (0.73–1.14)	2 (0.0%)	1.13 (0.89–1.43)
Micronutrients
	Magnesium			1 (NA)	0.87 (0.71–1.05)
	Potassium			1 (NA)	0.76 (0.56–1.04)			1 (NA)	0.99 (0.6801.43)	1 (NA)	1.10 (0.81–1.51)
	Calcium	2 (19.2%)	0.62 (0.43–0.90)
	Vitamin A			1 (NA)	0.99 (0.96–1.03)			1 (NA)	0.98 (0.95–1.01)	1 (NA)	0.97 (0.95–1.00)	1 (NA)	1.01 (0.95–1.02)
	Riboflavin	2 (0.0%)	1.16 (1.01–1.35)	2 (43.8%)	1.22 (0.96–1.54)
	Vitamin B6	2 (67.9%)	0.63 (0.32–1.22)
	Thymine (B1)	1 (NA)	0.91 (0.86–0.96)
	Niacin (B3)	1 (NA)	0.71 (0.64–0.78)
	Vitamin C	1 (NA)	0.34 (0.13–0.90)	1 (NA)	0.96 (0.93–1.00)			1 (NA)	1.58 (1.42–1.76)	1 (NA)	1.01 (0.97–1.04)	1 (NA)	0.98 (0.95–1.02)
	Flavonoids							1 (NA)	0.94 (0.79–1.12)	2 (51.2%)	0.96 (0.79–1.16)
	Sodium			4 (75.2%)	0.97 (0.76–1.24)
	Iodine			4 (54.5%)	1.05 (0.90–1.23)			4 (72.4%)	0.85 (0.69–1.04)	4 (49.5%)	0.84 (0.72–0.98)
	Amino acids	2 (0.0%)	0.82 (0.75–0.88)			2 (51.5%)	0.78 (0.66–0.93)	2 (0.0%)	0.84 (0.72–0.98)	2 (0.0%)	0.74 (0.63–0.88)	2 (38.8%)	0.81 (0.68–0.97)
Dietary indices/scores
	Modified Mediterranean diet score			1 (NA)	0.98 (0.73–1.33)			1 (NA)	0.70 (0.53–0.92)	1 (NA)	0.90 (0.71–1.15)
	Low carbohydrate diet score			2 (0.0%)	0.97 (0.77–1.22)	4 (82.2%)	0.89 (0.63–1.25)	4 (0.0%)	0.76 (0.65–0.90)	4 (0.0%)	0.76 (0.66–0.88)	2 (69.2%)	0.79 (0.41–1.51)
	Integrated Korean dietary pattern score			1 (NA)	0.85 (0.72–1.00)			1 (NA)	0.97 (0.89–1.06)	1 (NA)	1.03 (0.96–1.11)
	Korean healthy eating index	1 (NA)	1.27 (1.09–1.49)	6 (3.6%)	0.98 (0.97–0.98)	2 (0.0%)	0.98 (0.70–1.38)	4 (74.7%)	0.98 (0.97–0.99)	1 (NA)	0.99 (0.98–0.99)
	Modified recommended food score			1 (NA)	0.73 (0.59–0.91)
	Diet score			1 (NA)	1.36 (1.16–1.58)	1 (NA)	0.86 (0.71–1.06)
	Essential amino acid score			1 (NA)	0.86 (0.75–0.98)			1 (NA)	0.86 (0.76–0.98)	1 (NA)	0.96 (0.86–1.08)
	Dietary glycemic index			2 (66.2%)	1.11 (0.84–1.46)			2 (69.4%)	1.13 (0.83–1.53)	2 (0.0%)	1.55 (1.26–1.90)
	Overall plant-based diet index	2 (94.5%)	0.90 (0.69–1.18)	1 (NA)	0.95 (0.86–1.06)	1 (NA)	0.88 (0.71–1.10)	2 (0.0%)	0.95 (0.85–1.07)	2 (0.0%)	1.14 (1.04–1.25)	1 (NA)	1.03 (0.81–1.32)
	Healthful plant-based diet index	2 (98.3%)	0.80 (0.51–1.25)	1 (NA)	0.65 (0.57–0.75)	1 (NA)	0.80 (0.62–1.05)	1 (NA)	1.06 (0.85–1.32)	1 (NA)	0.87 (0.75–1.02)	1 (NA)	0.75 (0.57–1.00)
	Unhealthful plant-based diet index	2 (92.3%)	1.29 (1.01–1.66)	2 (84.5%)	1.25 (0.94–1.66)	1 (NA)	0.89 (0.70–1.12)	2 (0.0%)	1.44 (1.27–1.64)	2 (0.0%)	1.18 (1.06–1.31)	1 (NA)	0.89 (0.69–1.15)
	Dietary inflammatory index	3 (0.0%)	1.29 (1.10–1.51)	1 (NA)	1.24 (1.15–1.34)			1 (NA)	1.12 (1.14–1.35)	1 (NA)	1.63 (1.44–1.84)
	Phytochemical index			1 (NA)	0.82 (0.73–0.93			1 (NA)	0.84 (0.75–0.94)	1 (NA)	1.04 (0.94–1.15)
	Sodium to potassium ratio			2 (0.0%)	1.12 (1.02–1.24)			2 (50.2%)	1.13 (0.99–1.29)	2 (0.0%)	0.99 (0.91–1.08)
	Dietary protein to carbohydrate ratio			2 (0.0%)	1.01 (0.82–1.24)			2 (65.4%)	1.17 (0.77–1.62)	2 (0.0%)	0.90 (0.72–1.12)
	Dietary carbohydrate to fat ratio			3 (0.0%)	0.99 (0.98–1.00)			3 (0.0%)	0.99 (0.98–1.00)	4 (48.8%)	0.99 (0.97–1.00)
	Korean food pattern score			1 (NA)	0.90 (0.43–2.07)	1 (NA)	0.47 (0.28–0.79)	1 (NA)	0.79 (0.45–1.41)	1 (NA)	1.06 (0.74–1.51)
	Mean adequacy ratio	2 (0.0%)	0.83 (0.41–1.66)

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CVD, cardiovascular disease; BP, blood pressure; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; OR, odds ratio; HR, hazard ratio; CI, confidence interval; NA, not applicable.

Regarding macronutrient consumption, individuals with higher carbohydrate intake exhibited increased risks of CVD, elevated/high TG levels, and low HDL-C; however, protein intake was not associated with these outcomes. Fat intake was positively associated with elevated/high TC and elevated LDL-C. For micronutrient intake, calcium showed limited but inverse evidence of association with CVD risk. Additionally, iodine intake was negatively associated with low HDL-C levels.

Among dietary indices, negative associations were observed between a higher Korean Healthy Eating Index (KHEI) and elevated BP/hypertension (RR, 0.98; 95% CI, 0.98–0.98), as well as elevated/high TG (RR, 0.98; 95% CI, 0.97–0.99). Similarly, inverse associations were found between a low-carbohydrate diet score and elevated/high TG (RR, 0.76; 95% CI, 0.65–0.90) and low HDL-C (RR, 0.76; 95% CI, 0.66–0.88). Conversely, dietary glycemic index was positively associated with low HDL-C (RR, 1.55; 95% CI, 1.26–1.90).

In the analysis of dietary patterns, adherence to either a healthy dietary pattern (defined as a traditional dietary pattern) or an unhealthy dietary pattern (defined as a Western dietary pattern) was not significantly associated with CVD risk or elevated BP/hypertension (Figs. 2 and 3). Forest plots illustrating adherence to healthy and unhealthy dietary patterns concerning other lipid components—including TC, TG, HDL-C, and LDL-C—are presented in Supplementary Figs. 1-4. Adherence to a healthy dietary pattern showed a borderline significant relationship with reduced risk of elevated/high TG (RR, 0.90; 95% CI, 0.84–0.96; Supplementary Fig. 2). In contrast, adherence to an unhealthy dietary pattern was significantly associated with an increased risk of elevated/high TC (RR, 1.16; 95% CI, 1.05–1.28; Supplementary Fig. 1) and elevated LDL-C (RR, 1.14; 95% CI, 1.01–1.29; Supplementary Fig. 4).

Fig. 2 — OR, odds ratio; HR, hazard ratio; CI, confidence interval; KNCC, Korea National Cancer Screenee Cohort; KoGES, Korean Genome and Epidemiology Study.

Fig. 3 — OR, odds ratio; CI, confidence interval; KNHANES, Korean National Health and Nutrition Examination Survey; KNCC, Korea National Cancer Screenee Cohort; KoGES, Korean Genome and Epidemiology Study.

4. Subgroup analyses

Overall, the findings from the primary meta-analysis that included all study designs were largely consistent with those obtained from analyses stratified by study design (cross-sectional, case-control, or cohort studies) (Tables 2 and 3). However, although a significant association was observed between milk and dairy consumption and elevated BP/hypertension in the overall analysis, this association became borderline in the subgroup analysis limited to cross-sectional and case-control studies (Table 2). Additionally, associations between milk and dairy consumption and the risks of elevated/high TG and low HDL-C were significant in the subgroup analysis restricted to cohort studies, contrasting somewhat with findings from the overall analysis (Table 3).

Table 2. Meta analysis of cross-sectional and case-control studies regarding the associations of food items, nutrients, and dietary indices with cardiovascular diseases, hypertension, and lipid profile.

Variable		CVD		Elevated BP/hypertension		Elevated/high TC		Elevated/high total TG		Low HDL-C		High LDL-C
Variable		n (I²)	OR/HR (95% CI)	n (I²)	OR/HR (95% CI)	n (I²)	OR/HR (95% CI)	n (I²)	OR/HR (95% CI)	n (I²)	OR/HR (95% CI)	n (I²)	OR/HR (95% CI)
Food items
	Fruits/fruit juice	4 (0.0%)	0.74 (0.53–1.03)	8 (81.4%)	0.85 (0.77–0.95)	1 (NA)	0.85 (0.34–2.13)	6 (46.3%)	0.95 (0.88–1.02)	6 (38.1%)	1.03 (0.97–1.10)	1 (NA)	1.08 (0.95–1.23)
	Vegetables	3 (32.7%)	0.85 (0.72–0.99)	9 (70.8%)	0.93 (0.86–1.01)	1 (NA)	0.65 (0.28–1.49)	6 (30.4%)	0.92 (0.86–0.98)	6 (79.7%)	0.98 (0.87–1.10)	2 (33.4%)	0.99 (0.88–1.10)
	Fish
	Seafood			1 (NA)	0.54 (0.39–0.76)			1 (NA)	0.67 (0.51–0.88)	1 (NA)	0.66 (0.48–0.93)
	Sugar			3 (0.0%)	1.17 (1.07–1.28)	2 (0.0%)	1.24 (1.05–1.48)	3 (0.0%)	1.06 (0.98–1.16)	3 (35.0%)	1.02 (0.92–1.12)	2 (0.0%)	0.87 (0.74–1.03)
	Eggs	2 (5.2%)	1.01 (0.74–1.37)	4 (69.6%)	0.90 (0.78–1.04)	1 (NA)	1.08 (0.97–1.21)	2 (87.2%)	0.85 (0.71–1.03)	2 (34.4%)	0.84 (0.78–0.91)	3 (0.0%)	1.06 (0.99–1.12)
	Meat					1 (0.9%)	1.14 (1.06–1.22)					3 (66.3%)	1.01 (0.96–1.06)
	Milk and dairy	1 (NA)	0.26 (0.14–0.50)	7 (50.1%)	0.93 (0.87–0.99)			6 (0.0%)	0.86 (0.83–0.89)	6 (62.5%)	0.83 (0.77–0.89)	2 (0.0%)	1.05 (1.03–1.07)
	Sugar-sweetened beverages	1 (NA)	1.16 (0.97–2.67)	3 (0.0%)	1.21 (1.04–1.41)			2 (0.0%)	1.20 (0.99–1.45)	2 (90.6%)	1.06 (0.61–1.83)
	Coffee	6 (75.2%)	0.80 (0.64–1.01)	6 (42.3%)	0.91 (0.85–0.97)			6 (23.8%)	0.84 (0.79–0.90)	6 (86.5%)	0.88 (0.77–1.02)
	Fried foods			1 (NA)	1.56 (1.14–2.15)			1 (NA)	1.22 (0.92–1.62)	1 (NA)	0.85 (0.66–1.10)
	Colorful foods (rice, beans, fruits, vegetables)			4 (1.50%)	0.94 (0.90–0.98)			4 (0.0%)	0.98 (0.94–1.03)	4 (58.5%)	1.05 (0.97–1.15)
	Fat-rich processed foods	1 (NA)	2.44 (1.05–5.70)	1 (NA)	1.22 (1.09–1.37)							3 (12.2%)	1.04 (1.01–1.08)
	Nuts			2 (0.0%)	0.87 (0.79–0.96)	1 (NA)	0.65 (0.30–1.40)					2 (0.0%)	0.92 (0.82–1.03)
	Soy/fermented soy foods			3 (0.0%)	0.86 (0.76–0.98)			1 (NA)	0.93 (0.75–1.15)	1 (NA)	1.14 (0.98–1.34)
	Tea	1 (NA)	0.72 (0.59–0.88)
	Curry-rice	4 (75.1%)	0.46 (0.35–0.60)	1 (NA)	0.53 (0.48–0.59)	1 (NA)	0.91 (0.86–0.98)	1 (NA)	0.76 (0.71–0.81)	1 (NA)	0.89 (0.84–0.96)	1 (NA)	0.94 (0.83–1.06)
Macronutrients
	Carbohydrates	2 (68.7%)	2.04 (0.87–4.78)	6 (25.7%)	1.03 (0.93–1.15)	5 (0.0%)	0.80 (0.70–0.91)	7 (0.0%)	1.21 (1.09–1.35)	5 (27.9%)	1.21 (1.07–1.37)	3 (15.9%)	0.89 (0.76–1.05)
	Protein			1 (NA)	1.01 (0.67–1.53)	2 (0.0%)	0.83 (0.72–0.96)	3 (60.4%)	0.81 (0.65–1.00)	3 (81.8%)	0.90 (0.66–1.21)
	Fat	2 (73.2%)	1.01 (0.86–1.19)	2 (0.0%)	0.86 (0.77–0.95)	4 (0.0%)	1.25 (1.14–1.37)	1 (NA)	0.94 (0.93–0.96)	3 (0.0%)	0.87 (0.80–0.95)	2 (0.0%)	1.34 (1.15–1.56)
	Carbohydrate + protein			2 (44.3%)	1.13 (0.62–2.07)	2 (0.0%)	1.75 (1.09–2.82)	2 (21.2%)	1.11 (0.68–1.81)	2 (85.9%)	1.56 (0.53–4.61)	2 (23.9%)	1.37 (0.75–2.50)
	Fiber			2 (0.0%)	0.91 (0.73–1.14)	2 (0.0%)	1.13 (0.89–1.43)
Micronutrients
	Magnesium			1 (NA)	0.87 (0.71–1.05)
	Potassium			1 (NA)	0.76 (0.56–1.04)			1 (NA)	0.99 (0.68–1.43)	1 (NA)	1.10 (0.81–1.51)
	Calcium
	Vitamin A			1 (NA)	0.99 (0.96–1.03)			1 (NA)	0.98 (0.95–1.01)	1 (NA)	0.97 (0.95–1.00)	1 (NA)	1.01 (0.95–1.02)
	Riboflavin	2 (0.0%)	1.16 (1.01–1.35)	2 (43.8%)	1.22 (0.96–1.54)
	Vitamin B6
	Thymine (B1)	1 (NA)	0.91 (0.86–0.96)
	Niacin (B3)
	Vitamin C	1 (NA)	0.34 (0.13–0.90)	1 (NA)	0.96 (0.93–1.00)			1 (NA)	1.58 (1.42–1.76)	1 (NA)	1.01 (0.97–1.04)	1 (NA)	0.98 (0.95–1.02)
	Flavonoids							1 (NA)	0.94 (0.79–1.12)	2 (51.2%)	0.96 (0.79–1.16)
	Sodium			4 (75.2%)	0.97 (0.76–1.24)
	Iodine			3 (74.7%)	0.98 (0.74–1.30)			3 (74.9%)	0.82 (0.61–1.09)	3 (65.1%)	0.80 (0.63–1.03)
Dietary indices/scores
	Modified Mediterranean diet score			1 (NA)	0.98 (0.73–1.33)			1 (NA)	0.70 (0.53–0.92)	1 (NA)	0.90 (0.71–1.15)
	Low carbohydrate diet score			2 (0.0%)	0.97 (0.77–1.22)	4 (82.2%)	0.89 (0.63–1.25)	4 (0.0%)	0.76 (0.65–0.90)	4 (0.0%)	0.76 (0.66–0.88)	2 (69.2%)	0.79 (0.41–1.51)
	Integrated Korean dietary pattern score			1 (NA)	0.85 (0.72–1.00)			1 (NA)	0.97 (0.89–1.06)	1 (NA)	1.03 (0.96–1.11)
	Korean healthy eating index	1 (NA)	1.27 (1.09–1.49)	4 (0.0%)	0.98 (0.97–0.98)			2 (0.0%)	0.98 (0.97–0.98)	1 (NA)	0.99 (0.98–0.99)
	Modified recommended food score			1 (NA)	0.73 (0.59–0.91)
	Diet score			1 (NA)	1.36 (1.16–1.58)	1 (NA)	0.86 (0.71–1.06)
	Essential amino acid score			1 (NA)	0.86 (0.75–0.98)			1 (NA)	0.86 (0.76–0.98)	1 (NA)	0.96 (0.86–1.08)
	Dietary glycemic index			2 (66.2%)	1.11 (0.84–1.46)			2 (69.4%)	1.13 (0.83–1.53)	2 (0.0%)	1.55 (1.26–1.90)
	Overall plant-based diet index	1 (NA)	1.03 (0.99–1.07)			1 (NA)	0.88 (0.71–1.10)	1 (NA)	0.91 (0.75–1.10)	1 (NA)	1.16 (1.01–1.32)	1 (NA)	1.03 (0.81–1.32)
	Healthful plant-based diet index	1 (NA)	1.00 (0.96–1.04)			1 (NA)	0.80 (0.62–1.05)	1 (NA)	1.06 (0.85–1.32)	1 (NA)	0.87 (0.75–1.02)	1 (NA)	0.75 (0.57–1.00)
	Unhealthful plant-based diet index	1 (NA)	1.15 (1.11–1.20)	1 (NA)	1.08 (0.92–1.28)	1 (NA)	0.89 (0.70–1.12)	2 (0.0%)	1.44 (1.27–1.64)	2 (0.0%)	1.18 (1.06–1.31)	1 (NA)	0.89 (0.69–1.15)
	Dietary inflammatory index	2 (0.0%)	1.03 (0.58–1.83)
	Phytochemical index			1 (NA)	0.82 (0.73–0.93)			1 (NA)	0.84 (0.75–0.94)	1 (NA)	1.04 (0.94–1.15)
	Sodium to potassium ratio			1 (NA)	1.16 (1.02–1.32)			1 (NA)	1.05 (0.91–1.22)	1 (NA)	1.00 (0.88–1.14)
	Dietary protein to carbohydrate ratio
	Dietary carbohydrate to fat ratio
Dietary patterns
	Healthy pattern	2 (0.0%)	0.96 (0.85–1.09)	21 (62.1%)	1.03 (0.95–1.12)	5 (75.8%)	0.93 (0.83–1.05)	13 (84.8%)	0.88 (0.78–1.01)	13 (82.5%)	1.00 (0.90–1.12)	5 (79.9%)	1.02 (0.82–1.28)
	Unhealthy pattern	1 (NA)	1.10 (0.92–1.31)	12 (27.0%)	1.01 (0.93–1.10)	5 (69.8%)	1.09 (0.98–1.20)	11 (83.7%)	0.96 (0.84–1.09)	11 (37.5%)	0.93 (0.88–0.98)	6 (42.7%)	1.28 (1.11–1.49)

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Table 3. Meta analysis of cohort studies for the associations of food items, nutrients, and dietary indices with cardiovascular diseases, hypertension, and lipid profile.

Variable		CVD		Elevated BP/hypertension		Elevated/high TC		Elevated/high total TG		Low HDL-C		High LDL-C
Variable		n (I²)	OR/HR (95% CI)	n (I²)	OR/HR (95% CI)	n (I²)	OR/HR (95% CI)	n (I²)	OR/HR (95% CI)	n (I²)	OR/HR (95% CI)	n (I²)	OR/HR (95% CI)
Food items
	Fruits/fruit juice			4 (85.4%)	0.53 (0.37–0.75)			2 (40.4%)	0.54 (0.44–0.66)	2 (0.0%)	0.86 (0.76–0.97)
	Vegetables	1 (NA)	1.02 (0.99–1.06)	6 (19.1%)	1.01 (0.86–1.19)			2 (0.0%)	0.89 (0.72–1.10)	2 (0.0%)	0.93 (0.78–1.09)
	Fish			1 (NA)	0.61 (0.38–0.98)	2 (4.1%)	1.02 (0.94–1.11)	3 (56.9%)	0.83 (0.66–1.04)	3 (46.6%)	0.89 (0.65–1.22)	2 (43.1%)	1.11 (0.95–1.31)
	Seafood	2 (0.0%)	0.77 (0.60–0.97)	1 (NA)	1.28 (1.07–1.55)
	Eggs	1 (NA)	1.14 (0.87–1.49)	1 (NA)	0.79 (0.65–0.95)
	Meat	5 (73.4%)	1.10 (1.02–1.21)	1 (NA)	0.68 (0.56–0.83)	2 (76.4%)	1.20 (1.07–1.35)	2 (58.5%)	1.09 (0.95–1.27)	2 (0.0%)	0.95 (0.80–1.12)	2 (87.3%)	1.22 (0.99–1.51)
	Milk and dairy			4 (86.1%)	0.82 (0.69–0.97)			2 (90.6%)	0.68 (0.45–1.05)	2 (93.1%)	0.76 (0.49–1.17)
	Sugar-sweetened beverages			2 (0.0%)	1.19 (1.04–1.36)			1 (NA)	1.20 (0.91–1.60)	1 (NA)	1.55 (1.18–2.03)
	Coffee			2 (90.4%)	1.18 (0.65–2.15)			2 (17.3%)	0.81 (0.60–1.10)	2 (7.8%)	0.79 (0.57–1.09)
	Nuts			2 (0.0%)	0.73 (0.62–0.85)
	Soy/fermented soy foods	2 (69.8%)	0.67 (0.41–1.09)
Micronutrients
	Calcium	1 (NA)	0.62 (0.43–0.89)
	Vitamin B6	2 (67.9%)	0.63 (0.32–1.22)
	Niacin (B3)	1 (NA)	0.71 (0.64–0.78)
Dietary indices/scores
	Overall plant-based diet index	1 (NA)	0.78 (0.69–0.88)	1 (NA)	0.96 (0.86–1.06)			1 (NA)	0.98 (0.85–1.13)	1 (NA)	1.13 (0.99–1.27)
	Healthful plant-based diet index	1 (NA)	0.63 (0.56–0.70)	1 (NA)	0.65 (0.57–0.75)
	Unhealthful plant-based diet index	1 (NA)	1.48 (1.30–1.69)	1 (NA)	1.44 (1.24–1.67)
	Dietary inflammatory index	1 (NA)	1.31 (1.12–1.55)	1 (NA)	1.24 (1.15–1.34)			1 (NA)	1.12 (1.14–1.35)	1 (NA)	1.63 (1.44–1.84)
	Dietary protein to carbohydrate ratio			2 (0.0%)	1.01 (0.82–1.24)			2 (65.4%)	1.12 (0.77–1.62)	2 (0.0%)	0.89 (0.72–1.12)
	Dietary carbohydrate to fat ratio									1 (NA)	1.12 (0.99–1.27)
Dietary patterns
	Healthy pattern	4 (67.1%)	0.92 (0.56–1.51)	6 (42.8%)	1.06 (0.92–1.22)	5 (5.3%)	0.91 (0.85–0.98)	5 (0.0%)	0.85 (0.78–0.93)	4 (15.3%)	0.87 (0.76–1.00)	5 (0.0%)	0.97 (0.90–1.05)
	Unhealthy pattern	4 (73.1%)	0.80 (0.50–1.27)	2 (0.0%)	0.94 (0.78–1.13)	4 (90.6%)	1.21 (0.94–1.56)	4 (48.6%)	0.99 (0.88–1.13)	2 (43.6%)	1.01 (0.78–1.30)	5 (86.2%)	1.12 (0.93–1.36)

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Moreover, subgroup analyses based on the study population revealed a significant positive association between adherence to an unhealthy dietary pattern and the risk of elevated/high TC specifically in the HEXA (Urban) population (RR, 1.29; 95% CI, 1.10–1.52; Supplementary Fig. 1). Additionally, adherence to a healthy dietary pattern was associated with a 19% lower risk of elevated/high TG (RR, 0.81; 95% CI, 0.71–0.91; Supplementary Fig. 2). Furthermore, a borderline association between a healthy dietary pattern and low HDL-C was observed in the KNHANES study population (Supplementary Fig. 3).

5. Publication bias and sensitivity analyses

Supplementary Fig. 5 shows the Begg funnel plots and results from the Egger test evaluating publication bias in meta-analyses involving at least 5 studies. Except for associations between fruit intake and elevated BP/hypertension, unhealthy dietary pattern and elevated BP/hypertension, healthy dietary pattern and low HDL-C, and meat consumption and elevated LDL-C, all other analyses exhibited no evidence of significant publication bias (p>0.05). A leave-one-out sensitivity analysis was conducted to examine the robustness of the meta-analysis findings. Excluding individual studies sequentially did not substantially alter the pooled effect size, suggesting that no single study had a disproportionately large influence on the overall results (Supplementary Fig. 6).

DISCUSSION

In this updated systematic review and meta-analysis, we investigated the effects of dietary intake on CVD risk, BP, and lipid profiles by pooling effect sizes from 151 observational studies conducted in the Korean population. Most included studies reported data from nationwide surveys such as Ansan-Ansung, HEXA, KNCC, KNHANES, and KoGES. The majority of studies examined the association of dietary factors with outcomes such as CVD risk, elevated BP/hypertension, and lipid profile abnormalities, including elevated/high TC, elevated/high TG, low HDL-C, and elevated/high LDL-C. Dietary factors most frequently examined included fruits, vegetables, fish, seafood, sugar, eggs, meat, milk and dairy products, sugar-sweetened beverages, coffee, nuts, soy and/or fermented soy, tea, carbohydrate intake, and the KHEI. Despite variations in nomenclature across individual studies, dietary patterns derived from factor analyses of healthy and unhealthy foods were also identified and analyzed regarding their associations with the outcomes of interest.

Our analysis identified several dietary associations suggestive of potential cardiovascular benefits, such as higher fruit intake linked to lower elevated BP/hypertension risk, vegetable consumption associated with reduced risk of elevated/high TG, milk and dairy products associated with decreased elevated BP/hypertension risk, coffee intake associated with lower CVD risk, and adherence to the KHEI linked to lower elevated BP/hypertension risk. Conversely, consumption of sugar-sweetened beverages was positively associated with increased elevated BP/hypertension risk. High carbohydrate intake correlated with increased risks of CVD, elevated/high TG, and low HDL-C. Additionally, adherence to an unhealthy dietary pattern was positively associated with elevated/high TC.

Recent meta-analyses have supported our findings, concluding that high fruit and vegetable intake is associated with a lower risk of hypertension.¹⁷⁵ A dose-response meta-analysis further reported that increased fruit and vegetable consumption was associated with reduced risk of coronary heart disease, stroke, and overall CVD.¹⁵ Another recent meta-analysis of randomized controlled trials indicated that increasing fruit and vegetable intake beyond 3 servings per day could significantly improve CVD risk factors, particularly TG levels, especially when combined with other healthy dietary modifications.¹⁷⁶ Various biological mechanisms may explain the cardiovascular protective effects of fruits and vegetables. Soluble and insoluble dietary fibers can positively affect lipid assimilation and metabolism, thereby improving plasma lipid-lipoprotein profiles.¹⁷⁶,¹⁷⁷ Furthermore, micronutrients such as vitamins and minerals, along with bioactive compounds including flavonoids and polyphenols inherent in fruits and vegetables, have potent anti-oxidative and anti-inflammatory effects, potentially attenuating vascular dysfunction and inhibiting atherosclerotic progression.¹⁷⁶,¹⁷⁸

Our meta-analysis identified associations between high consumption of milk and dairy products and lower risks of elevated BP/hypertension and elevated/high TG. A recent dose-response meta-analysis reported a positive association between high-fat milk intake and CVD mortality, although overall dairy consumption was protective against CVD mortality.¹⁷⁹ Conversely, another meta-analysis involving 29 prospective cohort studies reported a neutral association between dairy intake and CVD mortality.¹⁸⁰ The biological mechanisms underlying these observations could involve dairy products’ high calcium, potassium, vitamin D, and phosphorus content, bioactive peptide effects, and probiotic activities.¹⁸¹

We also observed that higher coffee consumption was associated with reduced risks of CVD and elevated/high TG. A recent pooled analysis of prospective studies from the Asia Cohort Consortium similarly concluded that coffee intake was associated with a lower risk of CVD-related mortality.¹⁸² Additionally, a dose-response meta-analysis of cohort studies reported that consumption of approximately 4 cups of coffee daily was inversely associated with CVD mortality risk compared to non-consumers.¹⁸³ Several bioactive components in coffee—including chlorogenic acid, diterpenes, trigonelline, melanoidins, potassium, magnesium, and other polyphenols—have demonstrated anti-oxidative and anti-inflammatory properties, potentially mediating these beneficial effects.¹⁸²,¹⁸⁴,¹⁸⁵

Conversely, our findings indicated that sugar-sweetened beverage consumption was positively associated with increased risk of elevated BP/hypertension. Previous dose-response meta-analyses have similarly concluded that higher intake of sugar-sweetened beverages increases hypertension and coronary heart disease risks.¹⁸⁶ Another recent meta-analysis also suggested a significant association between sugar-sweetened beverage intake and increased risks of hypertension and stroke.¹⁸⁷ Biological mechanisms underlying these observations may include obesity-related cardiometabolic risks associated with these beverages. Sugar-sweetened beverages are rich in rapidly absorbable carbohydrates, such as fructose, which may exacerbate inflammation and oxidative stress.¹⁸⁷,¹⁸⁸

In examining dietary indices and patterns, we found that the KHEI was protective against hypertension, whereas adherence to an unhealthy dietary pattern was positively associated with elevated/high TC. A recent systematic review and meta-analysis concluded that plant-based diets significantly improved LDL-C levels among individuals at high CVD risk.¹⁸⁹ However, a recent meta-analysis of dietary patterns and CVD in Asian populations found no associations between plant-based, low-quality, animal-based, or diverse dietary patterns and CVD, highlighting the need for Asian-specific criteria for diet quality and patterns.¹⁹⁰

This study is an updated systematic review and meta-analysis extending a previously published comprehensive study to draw conclusions about associations between various dietary factors—including food items, macro- and micronutrients, dietary indices, and dietary patterns—and outcomes related to CVD, BP, and abnormal lipid profiles in the Korean population. Both similarities and differences are observed when comparing the findings of the previous meta-analysis to this updated analysis. Regarding similarities, high fruit intake consistently showed inverse associations with elevated BP/hypertension and elevated/high TG. High egg consumption was similarly associated with lower risks of elevated BP/hypertension and low HDL-C. Coffee intake maintained its inverse association with CVD risk and elevated/high TG, and high sugar-sweetened beverage consumption consistently increased risks of elevated BP/hypertension and elevated/high TG. Likewise, high carbohydrate intake correlated positively with elevated/high TG and low HDL-C, whereas fat intake showed an inverse association with elevated BP/hypertension. Additionally, negative associations persisted between high Mediterranean diet scores and elevated/high TG, as well as between low-carbohydrate diets and elevated/high TG or low HDL-C. Adherence to a healthy dietary pattern consistently showed an association with lower elevated/high TG risk. However, important differences emerged in this updated meta-analysis. The previous study identified an inverse association between high milk and dairy consumption and CVD risk, and a beneficial association between adherence to a healthy dietary pattern and reduced elevated/high LDL-C. In contrast, this updated meta-analysis has identified several new associations: higher vegetable intake was inversely associated with elevated/high TG; milk and dairy consumption showed inverse associations with elevated BP/hypertension, elevated/high TG, and low HDL-C; the KHEI demonstrated negative associations with elevated BP/hypertension and elevated/high TG; and adherence to unhealthy dietary patterns increased risks of elevated/high TC and elevated LDL-C.

The KHEI is a valuable tool for assessing dietary quality and its relationship with cardiometabolic health among Koreans, with higher adherence linked to lower cardiometabolic disease risks. The KHEI reflects a dietary pattern emphasizing nutrient-dense foods and limiting processed foods, added sugars, and sodium, which play pivotal roles in maintaining cardiometabolic health. Higher vegetable, dairy, and whole-grain intakes, along with lower sodium consumption, contribute to BP control. Reduced refined carbohydrate and sugar-sweetened beverage consumption enhances lipid profiles and aids in managing dyslipidemia. Thus, greater adherence to the KHEI is associated with a decreased incidence of cardiovascular diseases due to its emphasis on whole foods and healthy fats. Additionally, a positive period effect was noted with the KHEI, independent of age or cohort effects, suggesting a general improvement in diet quality over time across all age groups and birth cohorts. This improvement correlates with increased consumption of whole grains, dairy products, and protein-rich foods, along with decreased sodium intake.¹⁹¹ Moreover, the KHEI can aid in formulating and evaluating national nutrition policies and in epidemiological studies examining associations between overall dietary quality and chronic diseases.¹⁹²

A key strength of this comprehensive, updated systematic review and meta-analysis lies in its large sample sizes derived from nationwide surveys, particularly KNHANES, conducted by the Korea Center for Disease Control and Prevention, and other large prospective cohorts such as HEXA, KNCC, and KoGES. These extensive data sets with relatively longer follow-up durations contribute to robust findings applicable to the broader Korean population. However, several limitations merit consideration. First, among the 151 included studies, 94 were cross-sectional or case-control designs, providing a lower evidence level compared to prospective cohort studies. Furthermore, certain pooled effect estimates were derived from relatively smaller study populations, which introduced substantial between-study heterogeneity, reducing the strength of evidence from convincing to limited or insufficient. Second, although additional biomarkers indicative of atherosclerotic CVD in the Korean population—such as ankle-brachial index, high-sensitivity C-reactive protein, lipoprotein(a), apolipoprotein B, and coronary artery calcium score—have been reported,²,¹⁹³ these biomarkers were unavailable for inclusion in this meta-analysis. We acknowledge that our meta-analysis integrates studies reporting different effect measures (ORs, HRs, and RRs), potentially complicating interpretation. While original effect measures from the primary studies were retained, RR was used as a standardized interpretative framework to maintain consistency. Despite these variations, general trends and directions of associations remained consistent, supporting the robustness of our findings. Nevertheless, direct comparisons between effect sizes require caution, as ORs and HRs may not be directly equivalent to RRs in all contexts. Additionally, this study did not consider the degree of food processing, potentially influencing observed associations—especially regarding meat and coffee consumption. Future research should differentiate between minimally processed and ultra-processed foods. Lastly, data were insufficient to perform dose-response meta-analyses for all associations or to analyze effects of specific meat and fat subtypes.

In conclusion, this updated comprehensive meta-analysis provides evidence linking dietary intake with CVD and risk factors such as BP and lipid profiles. Associations suggestive of potential cardiovascular benefits were observed with higher intakes of fruits, vegetables, milk and dairy products, coffee, and adherence to the KHEI. Conversely, high consumption of sugar-sweetened beverages and adherence to unhealthy dietary patterns exhibited potentially harmful effects on CVD-related outcomes. Therefore, increased awareness of the specific roles of individual dietary components is crucial for the prevention of CVD and its associated risk factors.

Footnotes

Funding: This study was supported by a grant from the National Cancer Center of Korea (No. 24H1080).

Conflict of Interest: Jeongseon Kim is Deputy Editor of Journal of Lipid and Atherosclerosis since 2023 and Oh Yoen Kim is Editor of Journal of Lipid and Atherosclerosis since 2019. However, they were not involved in the peer reviewer selection, evaluation, or decision process of this article. The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript. No other potential conflicts of interest relevant to this article were reported.

Data Availability Statement: All data generated or analyzed during this study are included in this published article and its supplementary information files.

Author Contributions:

Conceptualization: Kim J, Gunathilake M, Hoang T.
Data curation: Kim J, Gunathilake M, Hoang T.
Formal analysis: Kim J, Gunathilake M.
Funding acquisition: Kim J.
Investigation: Kim J, Gunathilake M, Hoang T, Kim OY.
Methodology: Kim J, Gunathilake M, Hoang T, Kim OY.
Project administration: Kim J.
Supervision: Kim J.
Validation: Kim J, Gunathilake M, Hoang T, Kim OY.
Visualization: Kim J, Gunathilake M.
Writing - original draft: Kim J, Gunathilake M.
Writing - review & editing: Kim J, Gunathilake M, Hoang T, Kim OY.

SUPPLEMENTARY MATERIALS

Supplementary Table 1

Characteristics of individual studies included in the meta-analysis (n=151)

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Supplementary Table 2

Quality assessment of cross-sectional studies based on the Newcastle-Ottawa Scale

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Supplementary Table 3

Quality assessment of case-control studies based on the Newcastle-Ottawa Scale

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Supplementary Table 4

Quality assessment of cohort studies based on the Newcastle-Ottawa Scale

jla-15-111-s004.xls^{(37KB, xls)}

Supplementary Fig. 1

Meta-analysis of the associations of dietary patterns with elevated total cholesterol. (A) Healthy and (B) Unhealthy dietary patterns.

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Supplementary Fig. 2

Meta-analysis of the associations of dietary patterns with elevated triglycerides. (A) Healthy and (B) unhealthy dietary patterns.

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Supplementary Fig. 3

Meta-analysis of the associations of dietary patterns with low levels of high-density lipoprotein cholesterol. (A) Healthy and (B) unhealthy dietary patterns.

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Supplementary Fig. 4

Meta-analysis of the associations of dietary patterns with elevated levels of low-density lipoprotein cholesterol. (A) Healthy and (B) unhealthy dietary patterns.

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Supplementary Fig. 5

Publication bias for estimates in the meta-analysis of the associations between dietary intake and the risks of hypertension and dyslipidemia.

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Supplementary Fig. 6

Sensitivity analyses for estimates in the meta-analysis of the associations between dietary intake and the risks of hypertension and dyslipidemia.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Table 1

Characteristics of individual studies included in the meta-analysis (n=151)

jla-15-111-s001.xls^{(194.5KB, xls)}

Supplementary Table 2

Quality assessment of cross-sectional studies based on the Newcastle-Ottawa Scale

jla-15-111-s002.xls^{(45KB, xls)}

Supplementary Table 3

Quality assessment of case-control studies based on the Newcastle-Ottawa Scale

jla-15-111-s003.xls^{(29.5KB, xls)}

Supplementary Table 4

Quality assessment of cohort studies based on the Newcastle-Ottawa Scale

jla-15-111-s004.xls^{(37KB, xls)}

Supplementary Fig. 1

Meta-analysis of the associations of dietary patterns with elevated total cholesterol. (A) Healthy and (B) Unhealthy dietary patterns.

jla-15-111-s005.ppt^{(261KB, ppt)}

Supplementary Fig. 2

Meta-analysis of the associations of dietary patterns with elevated triglycerides. (A) Healthy and (B) unhealthy dietary patterns.

jla-15-111-s006.ppt^{(328.5KB, ppt)}

Supplementary Fig. 3

Meta-analysis of the associations of dietary patterns with low levels of high-density lipoprotein cholesterol. (A) Healthy and (B) unhealthy dietary patterns.

jla-15-111-s007.ppt^{(339.5KB, ppt)}

Supplementary Fig. 4

Meta-analysis of the associations of dietary patterns with elevated levels of low-density lipoprotein cholesterol. (A) Healthy and (B) unhealthy dietary patterns.

jla-15-111-s008.ppt^{(293.5KB, ppt)}

Supplementary Fig. 5

Publication bias for estimates in the meta-analysis of the associations between dietary intake and the risks of hypertension and dyslipidemia.

jla-15-111-s009.ppt^{(728.5KB, ppt)}

Supplementary Fig. 6

Sensitivity analyses for estimates in the meta-analysis of the associations between dietary intake and the risks of hypertension and dyslipidemia.

jla-15-111-s010.ppt^{(604KB, ppt)}

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PERMALINK

Associations of Dietary Intake With Cardiovascular Diseases, Blood Pressure, and Lipid Profile in the Korean Population: An Updated Systematic Review and Meta-Analysis

Jeongseon Kim

Madhawa Gunathilake

Tung Hoang

Oh Yoen Kim

Abstract

Objective

Methods

Results

Conclusion

INTRODUCTION

MATERIALS AND METHODS

1. Data sources and literature search

2. Inclusion and exclusion criteria

3. Data extraction

4. Quality assessment

5. Statistical analyses

RESULTS

1. Study selection

Fig. 1. Flow chart for study article selection.

2. Study characteristics and quality assessment

3. Main analyses

Table 1. Meta analysis of the associations of food items, nutrients, and dietary indices with cardiovascular diseases, hypertension, and lipid profile.

Fig. 2. Meta-analysis of the associations of dietary patterns with cardiovascular disease. (A) Healthy and (B) unhealthy dietary patterns.

Fig. 3. Meta-analysis of the associations of dietary patterns with elevated blood pressure/hypertension. (A) Healthy and (B) unhealthy dietary patterns.

4. Subgroup analyses

Table 2. Meta analysis of cross-sectional and case-control studies regarding the associations of food items, nutrients, and dietary indices with cardiovascular diseases, hypertension, and lipid profile.

Table 3. Meta analysis of cohort studies for the associations of food items, nutrients, and dietary indices with cardiovascular diseases, hypertension, and lipid profile.

5. Publication bias and sensitivity analyses

DISCUSSION

Footnotes

SUPPLEMENTARY MATERIALS

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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