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Although breakfast provides essential nutrients and energy, skipping this meal has become increasingly common among young adults in Korea. In this study, we examine the relationship between breakfast consumption and body composition.
Methods
We analyzed data from 17,763 students aged 18–39 at Seoul National University (2018–2022). Participants were categorized based on their breakfast frequency: non-skippers, 1 to 3-day skippers, and 4 to 7-day skippers. Measurements included body mass index, waist circumference, body fat percentage, fat mass index (FMI), fat-free mass index (FFMI), and skeletal muscle mass index (SMI). Multivariable logistic and linear regression models adjusted for age, sex, alcohol use, smoking, physical activity, sleep, and food consumption frequencies were used.
Results
Obesity (17.4% vs. 14.8%) and abdominal obesity (10.0% vs. 7.8%) were higher in those skipping breakfast 4 to 7 d/wk compared with non-skippers. Skipping breakfast was not significantly associated with abdominal obesity in either sex. In women, the odds of obesity were higher (odds ratio, 1.57; 95% confidence interval, 1.14–2.15; P=0.006), whereas no significant difference was observed in men. Men who skipped breakfast had increased body fat percentage (coefficient, 0.87; P<0.001) and FMI (coefficient, 0.18; P=0.009) and decreased FFMI and SMI. Women showed increased body fat percentage (coefficient, 0.92; P<0.001) and FMI but no significant differences in FFMI or SMI.
Conclusion
Skipping breakfast adversely affects body composition by increasing body fat percentage and FMI. Further research is needed to confirm these findings and explore the underlying mechanisms.
Breakfast is an important meal because it provides essential nutrients and energy to start metabolic processes after an overnight fast [1]. Numerous studies have highlighted its pivotal role in overall metabolic health, weight management, and cognitive function [2-4]. Despite these benefits, skipping breakfast has become increasingly common, particularly among adolescents and young adults. Data from the Korea National Health and Nutrition Examination Survey reported breakfast skipping rates as 35.2% in men and 32.8% in women in 2022. Breakfast skipping rates have steadily increased since 2013, with the highest rates observed among young adults in their 20s. In this age group, the rates increased from 43.2% to 55.4% in men and from 36.6% to 63.3% in women between 2013 and 2022 [5]. These trends are concerning because young adulthood is a critical life stage for establishing long-term eating habits and healthy lifestyles that can be maintained throughout life [6].
The relationship between breakfast consumption and body fat, such as obesity, a critical indicator of health status as well as a risk factor for various chronic diseases [7], has garnered substantial interest. Skipping breakfast has been associated with an increased risk of metabolic syndrome and cardiovascular diseases [8]. Potential explanations include disruptions in circadian rhythms, altered appetite regulation, and compensatory overeating later in the day [9]. However, most studies have used body mass index (BMI) as an outcome, which is limited in differentiating body fat and fat-free mass [10]. A randomized controlled trial meta-analysis on breakfast skipping and body composition found that individuals who did not consume breakfast showed decreased body weight. Despite this weight reduction, no significant differences were observed between the breakfast-skipping and breakfast-consuming groups concerning BMI, lean mass, fat mass, and changes in body fat percentage [11]. Notably, the number of participants in this meta-analysis was relatively small (n=425). While these findings provide some insights, the relationship between breakfast consumption and body composition remains complex. Recognizing the high rates of breakfast skipping among young adults, we propose a study focusing on this demographic. By utilizing a larger sample size, we aim to gain a more comprehensive understanding of the effects of breakfast consumption on body composition beyond what previous studies have revealed, focusing on BMI and body weight changes. Examining the relationship between breakfast frequency and body composition in young adults may yield valuable information about healthy meal patterns. Therefore, we aimed to investigate the association between breakfast consumption and body composition among young adults.
Methods
Study population
We used the health check-up data of 20,480 Seoul National University students, including undergraduate, graduate, and doctoral students, from 2018 to 2022. All students were eligible to voluntarily undergo a free annual health checkup consisting of a self-administered questionnaire on various health behaviors, anthropometric measurements (height, weight, and waist circumference), body composition analysis, and laboratory tests. We excluded (1) individuals under 18 or over 40 years of age (n=216), (2) foreigners (n=2,427) [12], (3) pregnant women (n=21), and (4) those with incomplete information (n=29). Ultimately, 17,763 participants were included. All individuals provided informed consent to participate in the study, which was approved by the Institutional Review Board (IRB) of Seoul National University (IRB approval no., C-1304-062-481). As it involved human participants, our research was conducted in accordance with the Declaration of Helsinki.
Anthropometric and laboratory measurements
Weight and height were measured with participants wearing light clothing on the day of the health checkup. Waist circumference was measured at the midpoint between the last rib and the top of the iliac crest. BMI was calculated as weight (kg) divided by height squared (m2). Obesity is defined as a BMI of 25 kg/m2 or higher according to the definition by the Korean Society of Obesity. Abdominal obesity is defined as a waist circumference of 90 cm or more for men and 85 cm or more for women. Blood pressure (BP) was measured with the participants in a sitting position using an automatic BP measurement system after a rest period of at least 5 minutes. Blood samples were collected after a fasting period of at least 12 hours.
Assessment of body composition
Body composition information was obtained using bioelectrical impedance analysis (InBody 770; InBody Co.). The InBody 770 sends a series of low-level electrical currents through the body. These currents flow through water, muscle, and fat at different rates owing to their varying resistance levels (impedance) [13]. Fat mass is calculated based on the resistance that electrical currents face as they pass through nonconductive fat tissue. Fat-free mass, which comprises muscle, bone, water, and other non-fat tissues, is calculated from the conductive components of the body, where the current passes more easily. Skeletal muscle mass, a fat-free mass component, is estimated based on empirical relationships developed from impedance measurements and statistical modeling. Body fat percentage is calculated by dividing fat mass by total body weight and multiplying the result by 100. To adjust for body size, the fat mass index (FMI), fat-free mass index (FFMI), and skeletal muscle mass index (SMI) were calculated as fat mass (kg), fat-free mass (kg), and skeletal muscle mass (kg), respectively, divided by height squared (m2).
Assessment of dietary intake
The participants completed a standardized food frequency questionnaire. Using the question “How often do you eat breakfast in a week?” (possible responses: from 0–7 days), participants were divided into three groups based on the frequency of skipping breakfast per week: non-skippers (having breakfast 7 days per week), 1 to 3-day skipper (having breakfast 4–7 days per week), 4 to 7-day skipper (having breakfast 0–3 days per week). To assess general meal patterns, the frequency of binge eating and meal regularity were evaluated. We also collected the frequency of consumption for each food group (refined grains, whole grains, snacks, fruits, vegetables, milk and dairy products, eggs, fish, high-fat meat, processed meat, and sugared beverages) with nine possible responses (rare, once a month, 2–3 times/mo, once a week, 2–4 times/wk, 5–6 times/wk, once a day, twice a day, and 3 times/d).
Other covariables
Information on various health behaviors was collected using self-administered questionnaires. Alcohol consumption was classified as non-drinker, moderate, or heavy drinker. Moderate drinking was defined as consuming 14 or fewer standard drinks per week for men and seven or fewer standard drinks per week for women, whereas heavy drinking was defined as exceeding these amounts (1 standard drink=12 g of alcohol). Smoking status was categorized as never, former, or current smoker. Physical activity levels were evaluated using the International Physical Activity Questionnaire [14]. Total physical activity levels were calculated as metabolic equivalent [MET]-minutes per week and categorized into low (<600 MET-min/wk), moderate (600–2,999 MET-min/wk), and high (≥3,000 MET-min/wk). Sleep duration was assessed and categorized as short (≤6 hours), normal (6–9 hours), or long (≥9 hours) [15].
Statistical analyses
Data are expressed as means with standard deviations for continuous variables or as numbers with percentages for categorical variables. Chi-square tests and analysis of variance were used to compare general characteristics of the study population by breakfast skipping status. Logistic regression analyses were performed to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for abdominal obesity and obesity. Linear regression analyses were used to assess associations with continuous variables, including FMI, FFMI, and SMI. As body composition ratios differ between men and women [16], we conducted separate analyses based on sex. Model 1 was adjusted for age, and Model 2 was additionally adjusted for alcohol consumption, smoking, physical activity, and sleep duration. Model 3 included further adjustments for the intake frequencies of refined grains, whole grains, snacks, fruits, vegetables, milk and dairy products, eggs, fish, high-fat meats, processed meats, and sugared beverages (<1 time per week, 1–4 times per week, ≥5 times a week). All statistical analyses were conducted using Stata ver. 18.0 for Windows (Stata Corp.). A P-value of <0.05 was considered statistically significant.
Results
General characteristics
Table 1 shows the differences in general characteristics across the three groups based on breakfast frequency. Participants’ mean age was 24.78±4.02 years. Compared with non-skippers, 4 to 7-day breakfast skippers were men (53.2%), heavy drinkers (15.5%), current smokers (8.5%), physically inactive (19.3%), slept longer (4.0%), more frequent binge eaters (≥3 times per week, 6.2%) and had irregular meals (89.9%). The prevalence of prehypertension and hypertension (19% versus 22.4%), hypertriglyceridemia (5.2% versus 8.4%), dyslipidemia (1.3% versus 14.5%) was higher in the 4 to 7-day breakfast skippers than in non-skippers. Similarly, the prevalence of prediabetes and diabetes mellitus was higher in 4 to 7-day breakfast skippers than in non-skippers (12.1% versus 14.4%).
Anthropometric measurements and body compositions
Table 2 shows the anthropometric measurements and body composition of the participants based on breakfast skipping frequency. Across the breakfast fasting groups, BMI was slightly higher in 4 to 7-day skippers than in non-skippers (22.2 kg/m2 versus 22.0 kg/m2). The prevalence of obesity (17.4% versus 14.8%), abdominal obesity (10.0 versus 7.8), and the rate of high body fat percentage (35.3% vs. 27.5%) were also higher in the 4 to 7-day breakfast skippers than in non-skippers. Among 4 to 7-day breakfast skippers, FMI was higher (5.7 kg/m2 versus 5.3 kg/m2) and FFMI (16.5 kg/m2 versus 16.7 kg/m2) and SMI (9.1 kg/m2 versus 9.2 kg/m2) were slightly lower than in non-skippers.
Association between breakfast skipping and body composition
Table 3 presents the association between breakfast skipping frequency and various body composition indices. In 4 to 7-day breakfast skippers, a higher body fat percentage (coefficient, 0.87; 95% CI, 0.48–1.25; P<0.001 in men and coefficient, 0.92; 95% CI, 0.56–1.28; P<0.001 in women) and FMI (coefficient, 0.18; 95% CI, 0.05–0.32; P=0.009 in men and coefficient, 0.27; 95% CI, 0.14–0.39; P<0.001 in women) were observed than in those who skipped breakfast less frequently. Similarly, 4 to 7-day breakfast skippers had a negative coefficient for FFMI (coefficient, –0.26; 95% CI, –0.36 to –0.16; P<0.001) and SMI (coefficient, –0.16; 95% CI, –0.22 to –0.10; P<0.001) among men. However, no significant association was observed with FFMI and SMI in women (P=0.17 and P=0.27, respectively). In addition, 1 to 3-day breakfast skippers had no significant difference in body fat percentage, FMI, FFMI, and SMI compared with non-skippers among men. However, women who skipped breakfast for 1–3 days had a positive coefficient for body fat percentage (coefficient, 0.41; 95% CI, 0.02–0.80; P=0.041) and FMI (coefficient, 0.14; 95% CI, 0.01–0.28; P=0.04) compared with non-skippers.
Association of breakfast skipping with BMI and abdominal obesity
Table 4 shows the associations between breakfast skipping frequency and abdominal obesity/obesity based on sex. In men, abdominal obesity was not significantly different across breakfast-skipping groups in the fully adjusted model (OR, 0.98; 95% CI, 0.82–1.19; P=0.88), but the 1 to 3-day breakfast skippers had a lower OR of abdominal obesity than in non-skippers (OR, 0.80; 95% CI, 0.65–0.98; P=0.032). Obesity was also not significantly different in 4 to 7-day skippers (OR, 0.92; 95% CI, 0.79–1.06; P=0.25), but 1 to 3-day skippers had a lower OR of obesity than in non-skippers (OR, 0.84; 95% CI, 0.72–0.99; P=0.033). In women, the OR of obesity was 1.52 (95% CI, 1.14–2.15; P=0.006) in 4 to 7-day breakfast skippers compared with non-skippers, but in abdominal obesity, no significant difference was observed (OR, 1.38; 95% CI, 0.91–2.10; P=0.13).
Discussion
In this study, compared with breakfast non-skippers, young adults who frequently skipped breakfast had higher body fat percentages and FMI in both men and women and lower FFMI and SMI in men. Our findings suggest that skipping breakfast can lead to adverse changes in body composition. Although we found weaker associations between breakfast skipping and general and abdominal obesity measures (BMI and waist circumference), FMI, FFMI, and SMI are important indicators of body composition and health status [17]. FMI is used to evaluate the risk of metabolic diseases related to obesity, whereas FFMI is an indicator of nutritional status and health conditions related to muscle loss [18]. In previous studies, SMI has been useful for assessing sarcopenia related to aging, which is closely associated with decreased muscle strength and reduced functionality in daily life [19,20].
Several biological mechanisms have been proposed to explain our observed findings. Regular breakfast consumption activates metabolic activity, increases energy expenditure [21], and stabilizes meal patterns throughout the day, thus reducing overeating and snacking [22]. This can decrease total energy intake and contribute to fat reduction [23]. Additionally, eating breakfast improves insulin sensitivity and regulates the secretion of appetite-controlling hormones (e.g., leptin), thereby positively affecting body composition [24-26] by promoting muscle synthesis and inhibiting fat accumulation [27]. Breakfast promotes metabolic flexibility, manages energy balance by optimizing energy consumption patterns, and enhances fat oxidation over fat storage, which is influenced by the circadian rhythm [28].
We also found that men who frequently skipped breakfast had lower FFMI and SMI, whereas no significant differences were observed in women. Possible reasons for this include the differences in hormonal effects and physical activity levels. Compared with women, men typically have higher muscle mass and elevated testosterone levels, which are crucial for muscle maintenance and development [29]. Therefore, skipping meals, particularly breakfast, can lead to hormonal imbalances affecting muscle mass and metabolism and potentially having a greater impact on men.
In men, the 1 to 3-day skipper group exhibited a lower OR for obesity and abdominal obesity. This finding was inconsistent among women, which showed no statistically significant difference between the non-skipper group and the 1 to 3-day skipper group. In addition, 1 to 3-day breakfast skippers had similar body composition as those who were non-skippers among men; however, women who skipped breakfast for 1 to 3 days showed higher body fat percentage and FMI than in non-skippers. These findings suggest a sex-specific impact on the association between breakfast frequency and body fat, including body composition. Men who skipped breakfast for 1 to 3 days may have a greater calorie reduction effect than non-skippers in addition to favorable metabolism related to having breakfast like non-skippers have. Moreover, non-significant findings in women who skipped breakfast for 1 to 3 days may be attributed to the significant impact of female hormones, such as estrogen and progesterone, on body weight and fat storage [30], which means that decreased total caloric intake may not directly influence weight.
The strengths of this study include its large sample size, which enhanced the statistical power and validity of our findings. Our participants were young adults, a demographic that has been relatively under-researched but is in a critical life stage for establishing long-term dietary habits. We adjusted for many potential confounders, such as alcohol consumption, smoking, physical activity, and various dietary factors. By exploring the link between breakfast consumption and body composition, this study adds to the existing literature by providing new insights into how meal patterns affect health.
However, this study has some limitations that should be considered. The cross-sectional design made establishing a cause-and-effect relationship between breakfast consumption and body composition difficult. For example, a reverse causation is possible between skipping breakfast and obesity. Additionally, there might have been other confounding factors that were not addressed in our study. Although we adjusted for various lifestyle factors, residual and unmeasured confounding factors may exist. For instance, individuals who frequently skip breakfast may engage in other unhealthy behaviors, whereas those who regularly eat breakfast are more likely to have healthier lifestyles that influence body composition. Self-reported dietary data might have introduced recall bias. Excluding certain groups (e.g., international students) could have limited the generalizability of our findings.
In conclusion, regularly consuming breakfast was associated with healthier body composition, such as lower body fat percentage, higher fat-free mass, and skeletal muscle mass. Our findings indicate the potential benefits of breakfast consumption for metabolic health and chronic disease prevention. This further underscores the importance of developing health strategies that promote regular breakfast consumption among young adults to mitigate the risk of metabolic diseases.
Notes
Conflict of interest
No potential conflict of interest relevant to this article was reported.
Funding
None.
Data availability
Contact the corresponding author for data availability.
a)None; moderate ≤14 standard drinks/wk for men and ≤7 standard drinks/wk for women; heavy >14 standard drinks/wk for men and >7 standard drinks/wk for women (1 standard drink, 12 g of alcohol).
b)Low <600 MET-min/wk; moderate 600–2,999 MET-min/wk; high ≥3,000 MET-min/wk.
Anthropometric measurements and body composition of participants based on breakfast skipping frequency
Variable
Total
Breakfast frequency (/wk)
P-value
7 Days (non-skipper)
4–6 Days (skipper for 1–3 days)
0–3 Days (skipper for 4–7 days)
Total
17,763
2,732 (15.4)
4,488 (25.3)
10,543 (59.4)
Weight (kg)
63.7±12.8
63.2±12.9
63.9±12.4
63.7±13.0
0.060
BMI (kg/m2)
<0.001
Underweight (<18.5)
1,744 (9.8)
293 (10.7)
404 (9.0)
1,047 (9.9)
Normal (≥18.5 to ≤23)
9,874 (55.6)
1,560 (57.1)
2,529 (56.4)
5,785 (54.9)
Overweight (>23 to ≤25)
3.237 (18.2)
475 (17.4)
890 (19.8)
1,872 (17.8)
Obese (>25)
2,908 (16.4)
404 (14.8)
665 (14.8)
1,839 (17.4)
Mean BMI (kg/m2)
22.1±3.3
22.0±3.2
22.1±3.2
22.2±3.4
<0.001
Waist circumference (cm)
<0.001
Normal (male <90, female <85)
16,168 (91.0)
2,519 (92.2)
4,156 (92.6)
9,493 (90.0)
Abdominal obesity (male ≥90, female ≥85)
1,595 (9.0)
213 (7.8)
332 (7.4)
1,050 (10.0)
Mean waist circumference (cm)
76.3±9.3
75.7±9.2
76.1±8.9
76.5±9.5
<0.001
Body fat ratio (%)
<0.001
Normal (male <25, female <30)
12,056 (67.9)
1,982 (72.5)
3,249 (72.4)
6,825 (64.7)
High body fat ratio (male ≥25, female ≥30)
5,707 (32.1)
750 (27.5)
1,239 (27.6)
3,718 (35.3)
Mean body fat ratio (%)
24.6±7.3
23.8±7.3
23.8±7.3
25.1±7.3
<0.001
Fat mass (kg)
15.7±6.0
15.0±5.9
15.2±5.7
16.0±6.2
<0.001
Fat mass index (kg/m2)
5.5±2.2
5.3±2.1
5.3±2.1
5.7±2.2
<0.001
Fat free mass (kg)
48.0±10.6
48.2±10.8
48.7±10.6
47.7±10.5
<0.001
Fat free mass index (kg/m2)
16.6±2.4
16.7±2.5
16.8±2.4
16.5±2.4
<0.001
Skeletal muscle mass (kg)
26.6±6.5
26.7±6.7
27.0±6.5
26.4±6.4
<0.001
Skeletal muscle mass index (kg/m2)
9.2±1.6
9.2±1.6
9.3±1.6
9.1±1.6
<0.001
Values are presented as mean±standard deviation or number (%).
BMI, body mass index.
Table 3.
Multivariable-adjusted coefficients and 95% CIs for body fat percentage, and body composition indices based on breakfast skipping frequency
Variable
Breakfast frequency (/wk)
Non-skipper
Skipper for 1–3 days
Skipper for 4–7 days
Coefficient (95% CI)
P-value
Coefficient (95% CI)
P-value
Male
Body fat percentage
Model 1
0 (Ref)
0.08 (–0.33 to 0.49)
0.697
1.52 (1.16 to 1.89)
<0.001
Model 2
0 (Ref)
0.07 (–0.34 to 0.47)
0.748
1.15 (0.79 to 1.51)
<0.001
Model 3
0 (Ref)
0.01 (–0.39 to 0.41)
0.954
0.87 (0.48 to 1.25)
<0.001
Fat mass index
Model 1
0 (Ref)
–0.01 (–0.15 to –0.14)
0.944
0.40 (0.27 to 0.53)
<0.001
Model 2
0 (Ref)
–0.01 (–0.15 to 0.13)
0.862
0.29 (0.16 to 0.42)
<0.001
Model 3
0 (Ref)
–0.04 (–0.18 to 0.11)
0.622
0.18 (0.05 to 0.32)
0.009
Fat free mass index
Model 1
0 (Ref)
–0.07 (–0.18 to 0.05)
0.253
–0.27 (–0.37 to –0.17)
<0.001
Model 2
0 (Ref)
–0.08 (–0.19 to 0.02)
0.129
–0.22 (–0.32 to –0.12)
<0.001
Model 3
0 (Ref)
–0.09 (–0.20 to 0.02)
0.116
–0.26 (–0.36 to –0.16)
<0.001
Skeletal muscle mass index
Model 1
0 (Ref)
–0.04 (–0.10 to 0.03)
0.314
–0.16 (–0.22 to –0.10)
<0.001
Model 2
0 (Ref)
–0.05 (–0.12 to 0.02)
0.160
–0.13 (–0.19 to –0.07)
<0.001
Model 3
0 (Ref)
–0.05 (–0.12 to 0.02)
0.129
–0.16 (–0.22 to –0.10)
<0.001
Female
Body fat percentage
Model 1
0 (Ref)
0.57 (0.19 to 0.96)
0.004
1.30 (0.96 to 1.64)
<0.001
Model 2
0 (Ref)
0.49 (0.10 to 0.87)
0.014
1.10 (0.76 to 1.45)
<0.001
Model 3
0 (Ref)
0.41 (0.02 to 0.80)
0.041
0.92 (0.56 to 1.28)
<0.001
Fat mass index
Model 1
0 (Ref)
0.19 (0.05 to 0.32)
0.006
0.37 (0.26 to 0.49)
<0.001
Model 2
0 (Ref)
0.17 (0.03 to 0.30)
0.014
0.33 (0.21 to 0.44)
<0.001
Model 3
0 (Ref)
0.14 (0.01 to 0.28)
0.040
0.27 (0.14 to 0.39)
0.009
Fat free mass index
Model 1
0 (Ref)
0.03 (–0.05 to 0.11)
0.468
–0.09 (–0.16 to –0.02)
0.013
Model 2
0 (Ref)
0.04 (–0.04 to 0.12)
0.302
–0.06 (–0.13 to 0.01)
0.080
Model 3
0 (Ref)
0.05 (–0.03 to 0.13)
0.258
–0.05 (–0.13 to 0.02)
0.170
Skeletal muscle mass index
Model 1
0 (Ref)
0.03 (–0.02 to 0.08)
0.275
–0.04 (–0.09 to 0.00)
0.050
Model 2
0 (Ref)
0.03 (–0.01 to 0.08)
0.170
–0.03 (–0.07 to 0.01)
0.200
Model 3
0 (Ref)
0.03 (–0.01 to 0.08)
0.165
–0.03 (–0.07 to 0.02)
0.270
Model 1 was adjusted for age; Model 2 was adjusted for age, alcohol consumption, smoking, physical activity, and sleep duration; and Model 3 was adjusted for age, sex, alcohol consumption, smoking, physical activity, sleep duration, intake of refined grain, whole grain, snacks, fruits, vegetables, milk and dairy products, egg, fish, high-fat meat, processed meat, and sugared beverage intake frequencies (<1/wk, 1–4 times/wk, ≥5 times/wk).
CI, confidence interval; Ref, reference.
Table 4.
Multivariable-adjusted ORs and 95% CIs for abdominal obesity and obesity based on breakfast skipping frequency
Variable
Breakfast frequency (/wk)
Non-skipper
Skipper for 1–3 days
Skipper for 4–7 days
OR (95% CI)
P-value
OR (95% CI)
P-value
Male
WC (male ≥90 cm, female ≥85 cm)
Model 1
1 (Ref)
0.83 (0.68–1.01)
0.070
1.24 (1.04–1.47)
0.020
Model 2
1 (Ref)
0.82 (0.67–1.01)
0.058
1.11 (0.93–1.32)
0.260
Model 3
1 (Ref)
0.80 (0.65–0.98)
0.032
0.98 (0.82–1.19)
0.880
Obesity, BMI ≥25.0 (kg/m2)
Model 1
1 (Ref)
0.88 (0.75–1.03)
0.100
1.11 (0.97–1.27)
0.130
Model 2
1 (Ref)
0.86 (0.72–1.01)
0.064
1.04 (0.91–1.20)
0.570
Model 3
1 (Ref)
0.84 (0.72–0.99)
0.033
0.92 (0.79–1.06)
0.250
Female
WC (male ≥90, female ≥85 cm)
Model 1
1 (Ref)
1.45 (0.93–2.26)
0.104
1.74 (1.17–2.58)
0.006
Model 2
1 (Ref)
1.44 (0.92–2.26)
0.110
1.67 (1.12–2.49)
0.012
Model 3
1 (Ref)
1.34 (0.85–2.10)
0.210
1.38 (0.91–2.10)
0.130
Obesity, BMI ≥25.0 (kg/m2)
Model 1
1 (Ref)
1.42 (1.01–1.99)
0.044
1.79 (1.32–2.41)
<0.001
Model 2
1 (Ref)
1.42 (1.01–2.00)
0.044
1.76 (1.30–2.39)
<0.001
Model 3
1 (Ref)
1.34 (0.95–1.89)
0.100
1.57 (1.14–2.15)
0.006
Model 1 was adjusted for age; Model 2 was adjusted for age, alcohol consumption, smoking, physical activity, and sleep duration; and Model 3 was adjusted for age, sex, alcohol consumption, smoking, physical activity, sleep duration, intake of refined grain, whole grain, snacks, fruits, vegetables, milk and dairy products, egg, fish, high-fat meat, processed meat, and sugared beverage intake frequencies (<1/wk, 1–4 times/wk, ≥5 times/wk).
OR, odds ratio; CI, confidence interval; WC, waist circumference; Ref, reference; BMI, body mass index.
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None; moderate ≤14 standard drinks/wk for men and ≤7 standard drinks/wk for women; heavy >14 standard drinks/wk for men and >7 standard drinks/wk for women (1 standard drink, 12 g of alcohol).
Low <600 MET-min/wk; moderate 600–2,999 MET-min/wk; high ≥3,000 MET-min/wk.
Table 2. Anthropometric measurements and body composition of participants based on breakfast skipping frequency
Values are presented as mean±standard deviation or number (%).
BMI, body mass index.
Table 3. Multivariable-adjusted coefficients and 95% CIs for body fat percentage, and body composition indices based on breakfast skipping frequency
Model 1 was adjusted for age; Model 2 was adjusted for age, alcohol consumption, smoking, physical activity, and sleep duration; and Model 3 was adjusted for age, sex, alcohol consumption, smoking, physical activity, sleep duration, intake of refined grain, whole grain, snacks, fruits, vegetables, milk and dairy products, egg, fish, high-fat meat, processed meat, and sugared beverage intake frequencies (<1/wk, 1–4 times/wk, ≥5 times/wk).
CI, confidence interval; Ref, reference.
Table 4. Multivariable-adjusted ORs and 95% CIs for abdominal obesity and obesity based on breakfast skipping frequency
Model 1 was adjusted for age; Model 2 was adjusted for age, alcohol consumption, smoking, physical activity, and sleep duration; and Model 3 was adjusted for age, sex, alcohol consumption, smoking, physical activity, sleep duration, intake of refined grain, whole grain, snacks, fruits, vegetables, milk and dairy products, egg, fish, high-fat meat, processed meat, and sugared beverage intake frequencies (<1/wk, 1–4 times/wk, ≥5 times/wk).
OR, odds ratio; CI, confidence interval; WC, waist circumference; Ref, reference; BMI, body mass index.
