Lipids and cardiovascular/metabolic health

Eating frequency is inversely associated with blood pressure and hypertension in Korean adults: analysis of the Third Korean National Health and Nutrition Examination Survey

Abstract

Background/objectives:

A lower eating frequency (EF) has been suggested to be important in the development of cardiovascular risk factors such as obesity and hyperlipidemia. However, the association between EF and blood pressure (BP) remains unclear.

Subjects/methods:

The aim of this study was to explore the association of EF with BP and hypertension after adjusting for confounding variables, including body mass index (BMI) and waist circumference (WC). This cross-sectional study used data from the Third Korean National Health and Nutrition Examination Survey. A total of 4625 subjects aged 19 years were included. To explore the association of EF with BP and hypertension, we performed multiple linear regression analyses and multiple logistic regression analyses for survey design, respectively.

Results:

EF was inversely associated with systolic BP (SBP) and diastolic BP (DBP). As EF increased from 2 to 3, 4 and 5 times per day, estimated adjusted means of both SBP and DBP decreased, showing a significant linear trend independent of obesity (SBP: 120.66, 120.23, 119.18 and 117.92 mm Hg, respectively; P<0.001; DBP: 78.36, 77.78, 77.25 and 76.50 mm Hg, respectively; P=0.004). The inverse association between EF and hypertension was gradually attenuated and significant after adjustment for confounding variables including BMI and WC (P=0.040).

Conclusions:

This study suggests that lower EF is significantly associated with higher BP, which may be partially mediated by the effect of central obesity. Further prospective studies are needed to verify this causal relationship.

Introduction

Hypertension, an important public health problem worldwide owing to its high prevalence, is a major preventable risk factor for cardiovascular disease and chronic renal disease.1, 2, 3, 4, 5 Lifestyle modification is an effective tool for lowering blood pressure (BP).1 Diet is a major lifestyle factor contributing to the development of hypertension and is modifiable if properly managed and educated.1, 6 The Dietary Approaches to Stop Hypertension (DASH) diet, which emphasizes fruit, vegetable and low-fat dairy product consumption and reduced sodium intake, is recommended to lower BP.1, 7, 8 Two key aspects of dietary behavior are considered modifiable: what we eat and how often we eat.9 Eating frequency (EF) is often reported as the sum of the number of meals and snacks consumed per day;9, 10 it is thought to be important in the development of obesity and other cardiometabolic risk factors. Increased EF was related to improved cholesterol profiles in some studies,11, 12, 13 and a recent study reported that decreased EF is indicative of greater 10-year increases in body mass index (BMI) and waist circumference (WC).14 Another cross-sectional study suggested that higher EF is associated with lower WC and reduced cardiometabolic risk factors, including fasting glucose, total cholesterol, low-density lipoprotein cholesterol and triglycerides, and that these associations are mediated by WC.15

As obesity and abdominal obesity are well-established risk factors for high BP,16, 17 EF is likely associated with BP; however, this association has not been extensively explored. A randomized crossover trial found that eating three meals per day led to approximately 6% lower systolic BP (SBP) and diastolic BP (DBP) after 8 weeks compared with consuming all daily energy needs in one large meal each day.18 Another study investigating the relationship between meal frequency and plasma cholesterol level reported that the age- and sex-adjusted mean SBP for subjects who consumed four or more meals per day was significantly lower than that for those who consumed one or two meals per day.12 However, no association between EF and BP was observed in other studies.15, 19, 20

In this study, we aimed to investigate the association of EF with BP and hypertension after adjusting for confounding variables, including BMI and WC, in a representative sample of Korean adults.

Subjects and methods

Study population

This cross-sectional study was based on data from the Third Korean National Health and Nutrition Examination Survey (KNHANES III) conducted by the Korean Ministry of Health and Welfare in 2005. KNHANES III was a nationwide representative study for non-institutionalized civilians in the Republic of Korea using a stratified, multistage clustered probability sampling design.21, 22 Of the 9004 participants of the Nutrition Survey, we excluded 2420 subjects aged younger than 19 years. Subjects without records on current use of hypertension medications, data on BP and data on frequency of meals or snacks per day were also excluded. Of the remaining 4900 subjects, we excluded the 14 participants who answered that their meal frequency was zero and those with incomplete data on potential confounding variables including smoking status, smoking amount (pack-years), alcohol consumption frequency, nutrient intake per day and anthropometric values such as WC and BMI. Another 85 women who were pregnant or had missing data on pregnancy were also excluded. The final analysis included 4625 subjects. All participants signed an informed consent form.

Measurement of variables

Measurement of BP and confirmation of hypertension

BP was measured by trained medical staff using a standardized technique. A cuff size appropriate for the subject’s arm circumference was chosen, and the subject sat in a chair for 5 min to relax before BP measurement. A sphygmomanometer (Baumanometer; WA Baum Co. Inc., Copiague, NY, USA) was used to measure BP three times. The average of the second and third BP measurements was used to identify hypertension,1 defined by SBP 140 mm Hg or DBP 90 mm Hg according to the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation and Treatment of High Blood Pressure (JNC 7) classification;1 patients who reported taking antihypertensive medications were also considered to have hypertension.

EF and dietary measurements

Three measures of frequency of eating occasions were investigated: meal frequency, snack frequency and EF, which was defined as the sum of the number of meals and snacks eaten per day. Meal frequency was determined using the question ‘Did you eat breakfast/lunch/dinner yesterday?’ Snack frequency was estimated by the question ‘How many times do you eat snacks per day?’ The subjects were also asked about the types of snacks eaten frequently. The words ‘meals’ and ‘snacks’ were not further defined for the participants. Meal frequency was categorized as one, two and three meals per day; snack frequency was categorized as none, one, two and three or more snacks per day; and EF was categorized as two or less, three, four and five or more per day. Categories were determined by considering the distribution of each measure in the study population, ensuring adequate numbers in each group. Daily energy and nutrient intake, including intake of total calories (kcal per day), sodium (mg per day), potassium (mg per day) and calcium (mg per day), were assessed using a 24-h recall method by a well-trained nutritionist or interviewer for the Nutrition Survey. The 24-h recall method is a cost-effective and applicable dietary assessment for characterizing the average intake of a population.23, 24 The nutrient adequacy ratio (NAR) was calculated for each of nine nutrients (protein, calcium, phosphorus, iron, vitamin A, thiamin, riboflavin, niacin and vitamin C) using the formula: NAR=The subject’s daily intake of a nutrient/recommended nutrition intake of that nutrient. The nine NAR values were then averaged to yield the mean adequacy ratio (MAR), which is an index of the overall diet quality.25 The MAR provides an index of the overall diet quality. A high MAR score implies high-quality diet.26

Other potential confounding variables

Trained medical staff measured height and weight by 0.1 cm and 0.1 kg, respectively, following standardized procedures at mobile examination centers. BMI was calculated as weight divided by height squared (kg/m2), and WC was measured, according to the World Health Organization guideline, at the midpoint between the inferior margin of the last rib and the crest of the ilium in a horizontal plane.27 Data on age; sex; current medications for hypertension, diabetes and dyslipidemia; smoking status; smoking amount (pack-years); usual alcohol consumption frequency (per month); stress levels (rare, a little, much, too much); sleep sufficiency (very much sufficient, fairly sufficient, somewhat insufficient, definitely insufficient); exercise frequency (per week); and physical activity assessed by the score on the International Physical Activity Questionnaire (IPAQ)28 were acquired by the Health Interview Survey. On the basis of the smoking status, participants were classified as never smokers, past smokers (had smoked 100 cigarettes during their lifetime but were not smoking currently) and current smokers (had smoked 100 cigarettes and were still smoking). Pack-years of smoking were calculated using answers to the questions about tobacco use, including assessment of the usual number of cigarettes smoked daily and the smoking duration.

Statistical analysis

All statistical analyses were conducted with Stata version 10.0 (Stata Corp. LP, College Station, TX, USA) using the survey data commands (svy) to account for cluster effects and sampling weights. All results are presented as weighted values. The baseline characteristics of the participants were compared according to EF group using the complex sample general linear model for continuous variables and Pearson’s χ2 test with Rao–Scott correction using F statistic for categorical variables. The nutritional characteristics of each EF group were presented as mean and s.e., and P-value for trend was obtained using complex sample general linear model after adjustment for age and sex. We explored the associations of EF, meal frequency and snack frequency with SBP and DBP using multiple linear regression analyses for survey design. We estimated the adjusted means and 95% confidence intervals (CIs) of SBP and DBP in each EF group and tested for linear trend across EF groups after adjusting for potential confounding variables including age, sex, smoking status, smoking amount (pack-years), usual alcohol consumption frequency (per month), exercise frequency (per week), IPAQ (metabolic equivalent of task-minutes per week), total calorie intake (kcal per day), sodium intake (mg per day), potassium intake (mg per day), calcium intake (mg per day), hypertension medication (yes or no), sleep sufficiency, stress levels, MAR, BMI and WC. In addition, the analyses were repeated, excluding subjects who reported taking medications for hypertension, diabetes or dyslipidemia. To investigate the association of EF, meal frequency and snack frequency with hypertension, we constructed three models and conducted analyses using the multiple logistic regression model for survey design to estimate the odds ratios (ORs) and 95% CIs for the presence of hypertension after adjusting for potential confounding variables. Model 1 was adjusted for age and sex. Smoking status, smoking amount, usual alcohol consumption frequency, exercise frequency, IPAQ, total calorie intake, sodium intake, potassium intake, calcium intake, sleep sufficiency, stress levels and MAR were added in model 2. To examine whether any associations were mediated by obesity, model 3 included additional adjustments for BMI and WC. Multiple logistic regression analyses were repeated for the subgroups stratified by the presence of abdominal obesity (WC 85 cm in women and 90 cm in men),29 and by the diet quality index using the median of MAR as the cut-off point. Logistic regression was used in the tests for trend. We tested the interaction between WC and the categories of EF, snack frequency and meal frequency by including the interaction terms in the multiple logistic regression models. The same methods were also used to test interactions between diet quality and the categories of EF, snack frequency and meal frequency. All statistical significance was defined by a two-tailed P-value <0.05.

Results

Baseline characteristics

EF was classified as 2 or less (9.28%), 3 (39.57%), 4 (35.37%) or 5 or more (15.78%) times per day. The (EF=3) group had the highest mean SBP and DBP, whereas the high EF group (EF 5) had the lowest mean SBP and DBP, with significant differences between groups (120.27±0.51 and 78.27±0.32 mm Hg vs 114.92±0.66 and 75.37±0.49 mm Hg, P<0.001). Similar results were also observed when subjects taking antihypertensive medication were excluded. Hypertension prevalence was significantly lower in the high EF group (EF 5) than in the lower EF groups (19.04%, high EF group (EF 5); 28.64%, (EF=3) group; 21.94%, low EF group (EF 2); P<0.001). The low EF group had a higher proportion of men and current smokers, and more frequent alcohol intake than the high EF group. Table 1 presents other baseline characteristics according to the EF groups.

Table 1 Baseline characteristics of the population according to the eating frequency

Nutritional characteristics of study participants

The mean values of total calorie, protein, fat, carbohydrate and crude fiber intake were significantly higher in the high EF group than in the low EF group. Further, water and mineral intake, including sodium, potassium and calcium, as well as vitamin intake, including vitamin A, carotene, retinol, thiamine, riboflavin and vitamin C, were significantly higher in the high EF group and lowest in the low EF group. MAR or the index of diet quality increased with increasing EF, showing a significant linear trend after adjusting for age and sex (P<0.001) (Table 2). In further analyses stratified by meal frequency (3 vs 1–2 times per day), frequent snack eaters consumed more potassium, retinol, thiamine and riboflavin regardless of the meal frequency. As snack frequency increased, MAR also showed a significant increasing trend in both groups stratified by meal frequency. However, sodium intake was not different between the groups stratified by snack frequency (Supplementary Table 1).

Table 2 Nutritional characteristics according to the eating frequency

Association of EF, meal frequency and snack frequency with SBP and DBP

In multiple linear regression analyses for survey design after adjusting for covariates (Figure 1), the estimated adjusted mean SBP and DBP decreased as EF increased from 5 or more times per day, showing a significant linear trend (SBP: 120.66, 120.23, 119.18 and 117.92 mm Hg, respectively; P-value for trend <0.001; DBP: 78.36, 77.78, 77.25 and 76.50 mm Hg, respectively; P-value for trend=0.004). As snack frequency increased from zero to three per day, estimated adjusted means of both SBP and DBP decreased, showing significant linear trends (SBP: 120.35, 119.5, 118.55 and 117.14 mm Hg, respectively; P-value for trend <0.001; DBP 77.93, 77.54, 76.56 and 75.94 mm Hg, respectively; P-value for trend=0.003). However, the increasing trend of SBP or DBP with decreasing meal frequency was not statistically significant (Figure 1). Repeated analyses after excluding subjects taking medication for hypertension, diabetes or dyslipidemia showed similar results (Supplementary Table 2).

Figure 1
figure1

Adjusted mean (95% CIs) of SBP and DBP according to eating frequency using multiple linear regression analyses for survey design (N=4625). The adjusted mean of each EF group was compared with that of the (EF=3) group by the Wald test (*P-value for trend <0.05, **P-value for trend <0.01). The adjusted mean of each snack frequency group was compared with that of the low snack frequency group (snack frequency=0) by Wald test (*P-value for trend <0.05, **P-value for trend <0.01). The adjusted mean of each meal frequency group was compared with that of the high meal frequency group (meal frequency=3) by Wald test (*P-value for trend <0.05, **P-value for trend <0.01). (a) Adjusted for age, sex, smoking status, smoking amount (pack-years), usual alcohol consumption frequency (per month), exercise frequency (per week), International Physical Activity Questionnaire score (metabolic equivalent of task (MET)-minutes per week), total calorie intake (kcal per day), sodium intake (mg per day), potassium intake (mg per day), calcium intake (mg per day), hypertension medication, sleep sufficiency, stress level, MAR, BMI and WC. (b) Plus additional adjustment for meal frequency. (c) Plus additional adjustment for snack frequency.

Association of EF, meal frequency and snack frequency with hypertension

In the multiple logistic regression analyses, after adjusting for covariates including BMI and WC, the adjusted ORs for hypertension were 1.046 (95% CI: 0.745–1.468), 0.856 (95% CI: 0.695–1.054) and 0.732 (95% CI: 0.555–0.965) in each EF group (EF=2, EF=4 and EF 5, respectively) compared with the (EF=3) group. As EF increased from 2 or less to 5 or more times per day, the ORs for hypertension decreased linearly in all models (P-value for trend: 0.016 in model 1, 0.021 in model 2 and 0.040 in model 3). The inverse association between EF and hypertension was gradually attenuated after adjustment for confounding variables including BMI and WC. Snack frequency was inversely related to the odds of hypertension. In the final model, the adjusted ORs for hypertension were 0.828 (95% CI: 0.672–1.020), 0.746 (95% CI: 0.552–1.007) and 0.618 (95% CI: 0.375–1.018) for the groups that had one, two and three snacks per day, respectively, compared with the group that had no snacks. Higher snack frequency also decreased the ORs for hypertension in all models, showing a linear trend (P-value for trend: 0.003 in model 1, 0.005 in model 2 and 0.009 in model 3). Eating one meal per day increased the ORs for hypertension compared to eating three meals per day; however, the associations were not statistically significant (Table 3).

Table 3 Multiple logistic regression analyses for the odds of hypertension according to the eating frequency

The effect of EF on hypertension stratified by abdominal obesity and diet quality

A differential effect of EF and snack frequency on hypertension in terms of abdominal obesity was not observed (P-value for interaction=0.518 and 0.762, respectively). However, in a stratified multiple logistic regression analysis according to abdominal obesity, EF and snack frequency were significantly associated with the odds of hypertension even after controlling for confounding variables such as BMI in the subgroup with abdominal obesity (P-value for trend: 0.02 and 0.009, respectively), but not in the subgroup without abdominal obesity. The interaction of EF and snack frequency with diet quality was not significant (P-value for interaction=0.461 and 0.817, respectively). However, in the stratified multiple logistic regression analysis according to diet quality, a marginally significant association between EF and the odds of hypertension and a significant relationship between snack frequency and the odds of hypertension were observed in the low diet quality group with MAR <50% (P-value for trend: 0.057 and 0.011, respectively), but not in the high diet quality group with MAR 50% (Table 4).

Table 4 Multiple logistic regression analyses for the odds of hypertension according to the eating frequency divided by the presence of abdominal obesitya and by the MARb

Discussion

In this study, we found that lower EF, after adjustment for total calorie intake, was associated with increased SBP and DBP independent of BMI and WC. Further, a statistically significant inverse relationship between EF and hypertension was observed. The high EF group (EF 5) had an estimated lower level of SBP by 3 mm Hg and of DBP by 2 mm Hg than the low EF group (EF 2). Participants who did not consume snacks had a higher level of SBP by 3 mm Hg and of DBP by 2 mm Hg than those who consumed snacks 3 or more times daily after controlling for total calorie intake. These effects are similar to those of moderate drinking or a low-salt diet for primary hypertension prevention.1 Prospective studies reported that a 2 mm Hg decrease in the average DBP in the population would reduce the risk of hypertension by 17%; stroke by 14%; and coronary heart disease by 6%.30 In addition, a 3 mm Hg decrement in SBP distribution would result in an 8% reduction in stroke risk, a 5% reduction in coronary heart disease risk and a 4% decrease in all-cause mortality.31

Previous studies have suggested an association between EF and cardiovascular risk factors such as lipid profile, obesity and glucose tolerance.11, 12, 13, 14, 15 However, few studies have investigated the association between EF and BP. An 8-week randomized crossover study documented that eating three meals per day lowered SBP and DBP by 6% compared with eating one large meal, which corresponds to our study findings.18 However, in that randomized crossover study, the BPs of subjects in the different meal frequency groups were measured at different times of day. Therefore, it is possible that an observed increase in BP in those who consumed one large meal per day may be owing to diurnal variation in BP. Another study reporting an observed inverse relationship between meal frequency and BP focused on the association of meal frequency with cholesterol level, suggesting that potential confounding variables influencing measurement of BP may not have been sufficiently considered.12 In this study, most of the participants (71.33%) had three meals per day; therefore, increases in EF appear to be attributed to increases in snack frequency. In the analyses of snack frequency and meal frequency, snack frequency but not meal frequency was negatively related to BP and hypertension. A previous study reported that participants who consumed three snacks per day had a greater decrease in BP than those who did not have a snack, although the difference was not significant, which may be owing to the small sample size.19 Another study showed that the inverse association between daily eating frequency, especially snack frequency, and BP in school children depends on BMI and body fat mass.32 Therefore, we performed further adjustment for BMI and WC, and the inverse association between EF and BP remained significant regardless of BMI and WC. The apparent discrepancy between the previous study in schoolchildren and our study may be owing to the different age groups studied.

The underlying mechanism of the association between EF and BP remains unclear, but may be partly mediated by obesity or central adiposity. Some studies have suggested that low EF is related to obesity, in particular central obesity.9, 10, 33, 34, 35 A recent study reported that lower snack frequency and EF were related to greater 10-year changes in BMI and WC in adolescent girls.14 Obesity is a well-known risk factor for hypertension.16, 36 In our study, the association between EF and hypertension attenuated after adjusting for BMI and WC. Furthermore, in subjects with abdominal obesity, a significant inverse association between EF and hypertension was observed, but these associations were not significant in subjects without abdominal obesity. These findings suggest that central adiposity is an important factor in the relationship between EF and hypertension. However, in the present study, the inverse association between EF and BP persisted even after controlling for BMI and WC; therefore, there may be another pathway independent of obesity.

Insulin resistance or hyperinsulinemia may also contribute to the association between EF and BP.37 Increasing meal frequency while controlling the total calorie intake is related to lower insulin concentrations in type 2 diabetes patients.38 In addition, a cross-sectional study showed that subjects who regularly eat three meals per day have a lower risk of metabolic syndrome and insulin resistance than irregular eaters who eat one or two meals per day.39 Reduced insulin sensitivity in irregular eaters with decreased EF, which induces a higher insulin response to meals, is related to overactivation of the sympathetic nervous system, excess angiotensinogen secretion and renal sodium retention.38, 39, 40 This effect could result in elevated BP and carry over to the fasting state, which may affect the risk of increased BP. These effects may be more prominent in obese subjects who are susceptible to insulin resistance. Taken together, data suggest that higher EF without a change in the total calorie intake may have a beneficial effect on insulin resistance and BP control.

In addition, other previous studies have found that subjects who ate more often were likely to select healthy foods, resulting in improved diet quality15 and increased daily intakes of vitamins A, C and E and beta carotene.41 In our study, the high EF group ate more fruits and vegetables, milk and dairy products and beverages such as coffee and tea, but less fried food than the lower EF group. They also consumed more potassium, calcium, vitamin A, carotene, retinol, thiamine, riboflavin and vitamin C than their counterparts. Eating more frequent snacks with the same meal frequency also increased daily intake of potassium, retinol, thiamine and riboflavin, but not of sodium (Supplementary Table 1). Hence, diet quality was improved with increasing EF or snack frequency. The inverse relationship between EF or snack frequency and hypertension was maintained in the group with low-diet quality, but not in the group with high-diet quality. Therefore, improved diet quality may also explain the relationship between higher EF or snack frequency and lower hypertension. However, the inverse association of EF or snack frequency with hypertension and BP was observed even after the adjustment for diet quality, suggesting that this relationship is independent of diet quality.

In the present study, dietary sodium intake, an important risk factor for hypertension,42 increased with increasing EF. However, the inverse association of EF with BP or hypertension was maintained regardless of the adjustment for daily sodium intake. These results suggest that lower EF may be associated with high BP or the odds of hypertension, independent of sodium intake.

Although the influence of EF on total calorie intake remains unclear, the weighted mean daily calorie intake (±s.e.) and BMI (±s.e.) of the study subjects were 2108.7 (±17.7) kcal per day and 23.6 (±0.06) kg/m2, respectively, and the daily total energy intake in the subjects with one or two meals per day with increasing snack frequency from 0 to 3 times was 1682.1±52.2 and 2079.5±167.6 kcal per day, respectively.43

In conclusion, these findings suggest that EF, especially snack frequency, and dietary components without changes in the total calorie intake may have an impact on hypertension.

This study has some limitations. First, this is a cross-sectional study, which limited its ability to reveal a causal relationship between EF and BP. Second, there was potential recall bias because diet and other lifestyle factors were based on information retrospectively collected by self-reported questionnaires. Third, antihypertensive medication was assessed by self-reported questionnaire, not confirmed by a physician, which could possibly affect the reported number of subjects with hypertension. Fourth, dietary variables were estimated by a single 24-h dietary recall instead of three 24-h dietary recalls. This might not reflect the true diet at the individual level, not considering day-to-day variation. Finally, the study participants were of single ethnic origin; therefore, the results must be generalized cautiously. However, to our knowledge, this is the first study to identify the association between EF and BP after adjusting for the effects of BMI and central obesity using a representative sample of the Korean population. Further prospective studies are needed to verify the causal relationship between EF and BP, and well-designed randomized clinical trials are needed to elucidate whether higher EF can improve BP control.

References

  1. 1

    Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo Jr JL et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA 2003; 289: 2560–2572.

  2. 2

    Klag MJ, Whelton PK, Randall BL, Neaton JD, Brancati FL, Ford CE et al. Blood pressure and end-stage renal disease in men. N Engl J Med 1996; 334: 13–18.

  3. 3

    Levy D, Larson MG, Vasan RS, Kannel WB, Ho KK . The progression from hypertension to congestive heart failure. JAMA 1996; 275: 1557–1562.

  4. 4

    MacMahon S, Peto R, Cutler J, Collins R, Sorlie P, Neaton J et al. Blood pressure, stroke, and coronary heart disease. Part 1, Prolonged differences in blood pressure: prospective observational studies corrected for the regression dilution bias. Lancet 1990; 335: 765–774.

  5. 5

    Stamler J, Stamler R, Neaton JD . Blood pressure, systolic and diastolic, and cardiovascular risks. US population data. Arch Intern Med 1993; 153: 598–615.

  6. 6

    Lee JS, Park J, Kim J . Dietary factors related to hypertension risk in Korean adults—data from the Korean national health and nutrition examination survey III. Nutr Res Pract 2011; 5: 60–65.

  7. 7

    Vollmer WM, Sacks FM, Ard J, Appel LJ, Bray GA, Simons-Morton DG et al. Effects of diet and sodium intake on blood pressure: subgroup analysis of the DASH-sodium trial. Ann Intern Med 2001; 135: 1019–1028.

  8. 8

    Sacks FM, Svetkey LP, Vollmer WM, Appel LJ, Bray GA, Harsha D et al. Effects on blood pressure of reduced dietary sodium and the Dietary Approaches to Stop Hypertension (DASH) diet. DASH-Sodium Collaborative Research Group. N Engl J Med 2001; 344: 3–10.

  9. 9

    Holmback I, Ericson U, Gullberg B, Wirfalt E . A high eating frequency is associated with an overall healthy lifestyle in middle-aged men and women and reduced likelihood of general and central obesity in men. Br J Nutr 2010; 104: 1065–1073.

  10. 10

    Bachman JL, Phelan S, Wing RR, Raynor HA . Eating frequency is higher in weight loss maintainers and normal-weight individuals than in overweight individuals. J Am Diet Assoc 2011; 111: 1730–1734.

  11. 11

    Jenkins DJ, Wolever TM, Vuksan V, Brighenti F, Cunnane SC, Rao AV et al. Nibbling versus gorging: metabolic advantages of increased meal frequency. N Engl J Med 1989; 321: 929–934.

  12. 12

    Edelstein SL, Barrett-Connor EL, Wingard DL, Cohn BA . Increased meal frequency associated with decreased cholesterol concentrations; Rancho Bernardo, CA, 1984–1987. Am J Clin Nutr 1992; 55: 664–669.

  13. 13

    Redondo MR, Ortega RM, Zamora MJ, Quintas ME, Lopez-Sobaler AM, Andres P et al. Influence of the number of meals taken per day on cardiovascular risk factors and the energy and nutrient intakes of a group of elderly people. Int J Vitam Nutr Res 1997; 67: 176–182.

  14. 14

    Ritchie LD . Less frequent eating predicts greater BMI and waist circumference in female adolescents. Am J Clin Nutr 2012; 95: 290–296.

  15. 15

    Smith KJ, Blizzard L, McNaughton SA, Gall SL, Dwyer T, Venn AJ . Daily eating frequency and cardiometabolic risk factors in young Australian adults: cross-sectional analyses. Br J Nutr 2011; 108: 1086–1094.

  16. 16

    Benetou V, Bamia C, Trichopoulos D, Mountokalakis T, Psaltopoulou T, Trichopoulou A . The association of body mass index and waist circumference with blood pressure depends on age and gender: a study of 10,928 non-smoking adults in the Greek EPIC cohort. Eur J Epidemiol 2004; 19: 803–809.

  17. 17

    Oda E, Kawai R . Body mass index is more strongly associated with hypertension than waist circumference in apparently healthy Japanese men and women. Acta Diabetol 2010; 47: 309–313.

  18. 18

    Stote KS, Baer DJ, Spears K, Paul DR, Harris GK, Rumpler WV et al. A controlled trial of reduced meal frequency without caloric restriction in healthy, normal-weight, middle-aged adults. Am J Clin Nutr 2007; 85: 981–988.

  19. 19

    Bertéus Forslund H, Klingström S, Hagberg H, Löndahl M, Torgerson JS, Lindroos AK . Should snacks be recommended in obesity treatment? A 1-year randomized clinical trial. Eur J Clin Nutr 2007; 62: 1308–1317.

  20. 20

    Poston WSC, Haddock CK, Pinkston MM, Pace P, Karakoc ND, Reeves RS et al. Weight loss with meal replacement and meal replacement plus snacks: a randomized trial. Int J Obes 2005; 29: 1107–1114.

  21. 21

    Korea Center for Disease Control and Prevention. The Third Korea National Health and Nutrition Examination Survey (KNHANES III): Analytic Guidelines 2006, Available at http://knhanes.cdc.go.kr/ (accessed 18 May 2011).

  22. 22

    Park S-H, Lee K-S, Park H-Y . Dietary carbohydrate intake is associated with cardiovascular disease risk in Korean: analysis of the third Korea National Health and Nutrition Examination Survey (KNHANES III). Int J Cardiol 2010; 139: 234–240.

  23. 23

    Karvetti RL, Knuts LR . Validity of the 24-hour dietary recall. J Am Diet Assoc 1985; 85: 1437–1442.

  24. 24

    Biró G, Hulshof KF, Ovesen L, Amorim Cruz JA . Selection of methodology to assess food intake. Eur J Clin Nutr 2002; 56 (Suppl 2), S25–S32.

  25. 25

    Madden JP, Goodman SJ, Guthrie HA . Validity of the 24-hr. recall. Analysis of data obtained from elderly subjects. J Am Diet Assoc 1976; 68: 143–147.

  26. 26

    Kant AK . Indexes of overall diet quality: a review. J Am Diet Assoc 1996; 96: 785–791.

  27. 27

    The World Health Organization Western Pacific Region, The International Association for the Study of Obesity, and The International Obesity Task Force. The Asia-Pacific Perspective: Redefining Obesity and its Treatment. Health Communications Australia Pty Limited: Sydney, Australia, 2000.

  28. 28

    Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE et al. International Physical Activity Questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 2003; 35: 1381–1395.

  29. 29

    Lee SY, Park HS, Kim DJ, Han JH, Kim SM, Cho GJ et al. Appropriate waist circumference cutoff points for central obesity in Korean adults. Diabetes Res Clin Pract 2007; 75: 72–80.

  30. 30

    Cook NR, Cohen J, Hebert PR, Taylor JO, Hennekens CH . Implications of small reductions in diastolic blood pressure for primary prevention. Arch Intern Med 1995; 155: 701–709.

  31. 31

    Whelton PK, He J, Appel LJ, Cutler JA, Havas S, Kotchen TA et al. Primary prevention of hypertension: clinical and public health advisory from The National High Blood Pressure Education Program. JAMA 2002; 288: 1882–1888.

  32. 32

    Barba G, Troiano E, Russo P, Siani A . Total fat, fat distribution and blood pressure according to eating frequency in children living in southern Italy: the ARCA project. Int J Obes (Lond) 2006; 30: 1166–1169.

  33. 33

    Ma Y, Bertone ER, Stanek 3rd EJ, Reed GW, Hebert JR, Cohen NL et al. Association between eating patterns and obesity in a free-living US adult population. Am J Epidemiol 2003; 158: 85–92.

  34. 34

    Kim S, Goh E, Lee DR, Park MS . The association between eating frequency and metabolic syndrome. Korean J Health Promot 2011; 11: 9–17.

  35. 35

    Ruidavets JB, Bongard V, Bataille V, Gourdy P, Ferrieres J . Eating frequency and body fatness in middle-aged men. Int J Obes Relat Metab Disord 2002; 26: 1476–1483.

  36. 36

    Frisoli TM, Schmieder RE, Grodzicki T, Messerli FH . Beyond salt: lifestyle modifications and blood pressure. Eur Heart J 2011; 32: 3081–3087.

  37. 37

    Sheehan MT, Jensen MD . Metabolic complications of obesity. Pathophysiologic considerations. Med Clin N Am 2000; 84: 363–385. vi.

  38. 38

    Jenkins DJ, Ocana A, Jenkins AL, Wolever TM, Vuksan V, Katzman L et al. Metabolic advantages of spreading the nutrient load: effects of increased meal frequency in non-insulin-dependent diabetes. Am J Clin Nutr 1992; 55: 461–467.

  39. 39

    Sierra-Johnson J, Unden AL, Linestrand M, Rosell M, Sjogren P, Kolak M et al. Eating meals irregularly: a novel environmental risk factor for the metabolic syndrome. Obesity (Silver Spring) 2008; 16: 1302–1307.

  40. 40

    Tack CJ, Smits P, Willemsen JJ, Lenders JW, Thien T, Lutterman JA . Effects of insulin on vascular tone and sympathetic nervous system in NIDDM. Diabetes 1996; 45: 15–22.

  41. 41

    Zizza CA, Arsiwalla DD, Ellison KJ . Contribution of snacking to older adults' vitamin, carotenoid, and mineral intakes. J Am Diet Assoc 2010; 110: 768–772.

  42. 42

    Shim E, Ryu HJ, Hwang J, Kim SY, Chung EJ . Dietary sodium intake in young Korean adults and its relationship with eating frequency and taste preference. Nutr Res Pract 2013; 7: 192–198.

  43. 43

    The Korean Nutrition Society. Dietary Reference Intakes for Koreans, 1st revision. The Korean Nutrition Society: Seoul, Republic of Korea, 2010.

Download references

Acknowledgements

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (No. 2012-0000998). M-SP, H-JY and SK conceived the study, and G-HP and JHY helped design the study. G-HP, SK and SHC analyzed data and performed statistical analysis. M-SP and SK drafted the manuscript, and SHC, JHY and H-JY helped with the revision of the manuscript. M-SP, H-JY and SK have primary responsibility for the final content. All authors read and approved the final manuscript.

Author information

Correspondence to H-J Yoon or M-S Park.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information

Supplementary Information accompanies this paper on European Journal of Clinical Nutrition website

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Kim, S., Park, G., Yang, J. et al. Eating frequency is inversely associated with blood pressure and hypertension in Korean adults: analysis of the Third Korean National Health and Nutrition Examination Survey. Eur J Clin Nutr 68, 481–489 (2014). https://doi.org/10.1038/ejcn.2014.9

Download citation

Keywords

  • eating frequency
  • blood pressure
  • hypertension
  • abdominal obesity

Further reading