The association of energy, macronutrients, and food sources consumption at dinner versus breakfast with obesity: The National Health and Nutrition Examination Survey (NHANES), 2003-2016

The association of energy, macronutrients, and food sources consumption at dinner versus breakfast with obesity: The National Health and Nutrition Examination Survey (NHANES), 2003-2016 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The association of energy, macronutrients, and food sources consumption at dinner versus breakfast with obesity: The National Health and Nutrition Examination Survey (NHANES), 2003-2016 wanying hou, weiqi wang, changhao sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4543116/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This study aimed to investigate the association of the difference of dietary consumption (energy, macronutrients, and foods) at dinner versus breakfast with obesity among U.S adults. Subjects/Methods: This study adopted the data from the National Health and Nutrition Examination Survey (2003–2016), involving a total of 27911 participants. The differences in the ratio of total energy and three macronutrients with six subgroups at dinner versus breakfast (ΔRatio) were categorized into quartiles. The differences in the consumption of 17 types of food at dinner versus breakfast (ΔFoods) were considered as continuous variables. Body Mass Index (BMI) and Waist circumference (WC) were used to define general obesity (30.0 ≤ BMI 102 cm for men or WC > 88 cm for women). Multiple logistic and linear regression models were developed. Results: After a variety of covariates were adjusted, participants in the highest quartile (higher energy/macronutrient intake at dinner than breakfast) of ΔRatio in terms of energy, fat, saturated fatty acids (SFA), and unsaturated fatty acids (USFA) was positively associated with morbid obesity compared with those in the lowest quartile (OR ΔRatio of energy 1.27, 95%CI 1.01;1.61; OR ΔRatio of fat 1.27, 95%CI 1.01;1.60; OR ΔRatio of SFA 1.27, 95%CI 1.01;1.59; OR ΔRatio of USFA 1.28, 95%CI 1.02;1.59). The highest quartile of ΔRatio of low-quality carbohydrate led to higher odds of abdominal obesity (OR ΔRatio of low−quality carbohydrate 1.16; 95%CI 1.03–1.31). Meanwhile, ΔRatio of low-quality carbohydrates was significantly positively associated with BMI (coefficient: 0.562, 95%CI: 0.217–0.907). ΔFoods including whole fruits, other starchy vegetables, added sugars, poultry, dairy, and nuts were positively associated with obesity. Conclusions: This study indicated that among US adults, higher intake of energy, macronutrients (low-quality carbohydrate, fat, SFA, and USFA), and foods (whole fruits, other starchy vegetables, added sugars, poultry, dairy, and nuts) at dinner than breakfast was associated with higher odds of having obesity. In conclusion, this study emphasized the importance of diet quality and meal-timing in the prevention of obesity. energy macronutrients food sources energy distribution throughout a day obesity meal timing Figures Figure 1 Figure 2 Background Obesity is one of the biggest public health concerns due to its high prevalence and association with various chronic diseases [ 1 – 4 ]. Evidence suggested that high energy consumption was a relevant aspect of nutrition and maybe a major reason for obesity [ 5 – 8 ]. However, limited studies were conducted on the association of meal timing of energy, macronutrients, and food sources distribution during a day with obesity. Previous studies showed that energy distribution had an effect on obesity through disrupting clock gene expression [ 9 , 10 ], and the circadian clock gene played a critical role in energy balance for mammals [ 11 ]. Meanwhile, breakfast skipping [ 12 , 13 ], late lunch [ 14 ], and high energy intake at dinner [ 5 – 8 ] were associated with a higher risk of obesity, as well as lower overall diet quality and poorer cognitive performance [ 15 , 16 ]. Researchers reported that breakfast skipping was related to weight gain as a result of overcompensating energy-dense high-fat foods later in the day [ 17 ]. On the other hand, observational studies reported that breakfast consumption was inversely associated with obesity and chronic disease through the regulation of glycemia, insulinemia, lipid metabolism, and possible appetite [ 18 ]. Meal timing can also be employed to prevent obesity and other metabolic pathologies [ 9 ]. Beyond the quantity, limited research was conducted on the quality and food sources of macronutrients at dinner versus breakfast among adults. Evidence suggested that quality and food sources of macronutrients were also closely related to human health [ 19 – 21 ]. In the current study, we aimed to examine the association of the difference of dietary consumption (energy, macronutrients, and foods) at dinner versus breakfast with obesity among adults, and the data were collected from the National Health and Nutrition Examination Survey (NHANES, 2003–2016). Methods Study population The NHANES is a stratified and multistage study with a nationally representative sample of the population of the U.S [ 22 ]. Detailed information was previously provided [ 23 ]. A total of 71058 participants were included from 2003–2004 to 2015–2016 in NHANES. This study excluded participants without dietary data during the 7 cycles (N = 38838) and adults who were less than 20 years old (N = 2222). The participants with extreme energy consumption [ 24 ] ( 3,500kcal/day for women and 4,200 kcal/day for men) (N = 1111), pregnant women (N = 609), and those with missing value on current drinking, current smoking, and BMI were excluded in this study (N = 367). At last, a total of 27911 participants were included. The flow chart of the screening process for selecting eligible participants is shown in Supplementary Fig. 1. The NHANES protocol was approved by the Ethics Review Board of the National Center for Health Statistics Research, and all participants provided informed consent. Dietary assessment Participants’ food intakes for two nonconsecutive days were collected through 24-hour dietary recall interviews. The first 24-hour dietary recall was conducted in person, and the second one was conducted 3–10 days later via telephone. Dietary nutrients and energy consumption were estimated with the USDA’s Food and Nutrient Database for Dietary Studies (FNDDS), and the mean values of energy, nutrients, and foods consumption for day one and day two of the 24 h dietary recall were used in analyses. Based on the MyPyramid Equivalents Database 2.0 for USDA Survey Foods (MPED 2.0), the dietary intake component of the NHANES was integrated into 37 MyPyramid major groups and subgroups. Similar kinds of foods were combined into the same group according to the research published on JAMA [ 25 ]. Finally, a total of 4 main food groups and 17 subcategories were analyzed, which were high-quality carbohydrates including whole grains, legumes, whole fruits, and non-starchy vegetables; low-quality carbohydrates including refined grains, fruit juice, potato, other starchy vegetables, and added sugars; animal protein including red meat, processed meat, poultry, marine food, dairy, and eggs; plant protein including whole grains, refined grains, legumes, nuts, and soy. Food sources of fat were not examined for the reason that they are similar to protein food sources, and the existing evidence on fat is mainly focused on the types of fatty acids rather than food sources [ 26 ]. Details of the 4 main food groups were presented in Supplementary Table 1. Participants’ meal timing of breakfast, lunch, and dinner was self-reported. Four snack patterns were determined based on the timing of snacks consumed, including 1) snack between breakfast and lunch, which was the self-reported foods consumption between breakfast and lunch, 2) snack between lunch and dinner, which was the self-reported foods consumption between lunch and dinner, 3) snack after dinner, which was the self-reported foods consumption after dinner, and 4) none of the above, which means that the participants had never consumed foods besides three meals throughout a day [ 27 ]. Main exposure The primary exposure variable in this study was the difference in the ratio of total energy and macronutrient consumption at dinner versus breakfast throughout the day. For example, the way of calculation was as follows: the ratio of energy at dinner = energy consumption at dinner/total energy; the ratio of energy at breakfast = energy consumption at breakfast/total energy; ΔRatio of energy = the ratio of energy at dinner - ratio of energy at breakfast; The ratio of high-quality carbohydrates at dinner = high-quality carbohydrates consumption at dinner/total high-quality carbohydrates; the ratio of high-quality carbohydrates at breakfast = high-quality carbohydrates at breakfast/total high-quality carbohydrates; ΔRatio of high-quality carbohydrates = the ratio of high-quality carbohydrates at dinner – the ratio of high-quality carbohydrates at breakfast. The macronutrients examined in this study included carbohydrates (high-quality carbohydrates and low-quality carbohydrates), fat (saturated fatty acids [SFA] and unsaturated fatty acids [USFA, sum of polyunsaturated and monounsaturated fatty acids]), and protein (animal protein and plant protein). The second exposure was the difference in 17 subcategories of food consumption between dinner and breakfast, for example, ΔWhole grains = whole grains consumption at dinner – whole grains consumption at breakfast. Main outcome In terms of the outcomes, general, morbid, and abdominal obesity were determined by BMI and WC, respectively. Height and weight were measured by standardized procedures [ 28 ], in which participants were asked to remove their hair ornaments, jewelry, and other accessories from the head, and whether the back of the head, shoulder blades, buttocks, and heels made contact with the backboard was checked. Participants were weighed in kilograms with a digital weight scale and they should wear a standard MEC (mobile examination center) examination gown, which consists of a disposable shirt, pants, and slippers. At the end of the examination, the weight of participants was displayed in both kilograms and pounds. BMI was calculated by the weight over height squared (kg/m2) and used to define general obesity (30.0 ≤ BMI 102 cm for men or WC > 88 cm for women) [ 30 ]. A further detailed description of examination protocol, quality control, and safety procedures can be found in the Anthropometry Procedures Manual via the NHANES website. Assessment of covariates Potential covariates were age (years old), sex (male/female), race/ethnicity (non-Hispanic white/non-Hispanic black/Mexican American/other), education level (< Grade 9/Grade 9–11/high school graduate/GED or equivalent/some college or Associate degree/college graduate or above), annual household income ( $ 100,000), regular exercise (in a typical week, doing some moderate-intensity sports, fitness, or recreational activities that cause a small increase in breathing or heart rate for at least 10 minutes continuously, yes/no), current smoker (yes/no), current drinker (had at least 12 alcohol drinks/1year, yes/no), medicine use for lower blood sugar, medicine use for hypertension, medicine use for cholesterol, total intake of energy (kcal/day), fat (g/day), protein (g/day), SFA (g/day), dietary fiber (g/day), dietary cholesterol (mg/day), high-quality carbohydrates (cups/ounce/tsp equivalent) and animal protein (cups/ounce/tsp equivalent), daily total consumption of foods (cups/ounce/tsp equivalent), dietary supplements use (yes/no), breakfast skipping (energy consumption at breakfast was 0 kcal at the two days’ 24-hours dietary recall), and diet quality calculated by the Alternative Healthy Eating Index (AHEI) [ 31 ]. Statistical analyses All analyses incorporated the dietary sample weights, stratification, and clustering of the complex sampling design to ensure the nationally representative estimates according to NHANES analytic guidelines. In addition, to correct the measurement error, the absolute intakes of macronutrients and food sources per day were adjusted for total energy intake by the residual method in dietary estimates [ 32 ]. The differences in the ratio of total energy and macronutrient consumption at dinner versus breakfast (ΔRatio = the ratio at dinner – the ratio at breakfast), the ratio at breakfast, and the ratio at dinner were categorized into quartiles. The differences in food consumption between dinner and breakfast (ΔFoods), foods at breakfast, and foods at dinner were considered as continuous variables. Demographic characteristics, dietary nutrient intake, and anthropometric measurements were presented as the mean ± standard error for continuous variables and the percentage of categorical variables. The differences of ΔRatio of energy by quartiles were tested with general linear models for all variables after the age adjustment. Multiple logistic regression models were developed to examine the association of ΔRatio, the ratio at breakfast, the ratio at dinner, ΔFoods, foods at breakfast, foods at dinner, with general obesity, morbid obesity, and abdominal obesity. Odds ratios (ORs) and 95% confidence intervals (CIs) were provided. Categorical variables were modeled as continuous variables through the assignment of the median value to each quartile to test linear trends. Models were adjusted for age, sex, ethnicity, education level, annual household income, regular exercise, current smoker, current drinker, the medicine used for lower blood sugar, the medicine used for hypertension, the medicine used for cholesterol, total intake of energy, fat, protein, SFA, dietary fiber, dietary cholesterol, high-quality carbohydrate, animal protein, dietary supplements use, breakfast skipping, and AHEI. Linear regression models were performed to examine the association of ΔRatio of energy and macronutrients with BMI (BMI was not log-transformed, since it was an approximately normal distribution, as shown in Supplementary Fig. 2). For the associations of ΔFood, food at breakfast, and dinner with obesity, models were an additional adjustment for the total daily intake of the food consumption. General linear regression models were also adopted to examine the difference in macronutrients consumption during the day among participants. This study further explored the substitution effects on obesity by replacing the food at dinner with 1 equivalent unit of food consumption at breakfast. The food consumption at breakfast and dinner were included in the same multivariable models as continuous variables. For each substitution at dinner with breakfast, the difference between the β coefficients of the 2 variables was adopted to estimate the OR, and the variances and covariance of the 2 variables were adopted to estimate the 95% CI [ 33 ]. The differences in the estimates statistically predicted the substitution effects on the risk of general obesity, morbid obesity, and abdominal obesity. This substitution was interpreted as the association of decreasing 1 equivalent unit of food consumption at dinner and simultaneously increasing the intake at breakfast with obesity. The chart showing all the analyses performed in this study is shown in Supplementary Fig. 3. Sensitivity analyses Thirteen sensitivity analyses were performed in ΔRatio in terms of energy and macronutrients and obesity with multiple logistic regression models. (1) Participants with breakfast skipping were excluded. (2) Participants with dinner skipping were excluded. (3) The snack consumption between breakfast and lunch as well as snack after dinner were considered (Δ= (dinner ་ snack after dinner) – (breakfast ་ snack between breakfast and lunch)). (4) Macronutrients in the energy-adjusted form were calculated [ 34 ] (macronutrients were measured in units per 1000 kcal per day, and details were shown in Supplementary methods 1). (5) Participants were stratified into tertiles with total energy consumption (tertile1 included participants with lowest energy consumption, tertile2 included participants with middle energy consumption, and tertile3 included participants with highest energy consumption). (6) Participants who were night shift workers or rotating workers from 2005 to 2010 were excluded. (7) Further adjustment was made with sleep hours from 2005 to 2016. (8) Further adjustment was made with participants who did not consume both breakfast and snacks after breakfast. (8) We investigated the above associations among participants without excluding those with extreme energy intake. (9) Additionally adjusted with timing for breakfast and dinner. After Bonferroni correction for multiple comparisons in cox regression models, a two-sided P < 0.01 was considered to be statistically significant. All analyses were performed with R 3.6.1. Results Characteristics of participants Table 1 illustrates the characteristics among participants aged over 20 from NHANES 2003–2016 in this study (N = 27911). Among the 27911 participants, 8611 had general obesity, 1863 had morbid obesity, and 16007 had abdominal obesity. Compared with participants in quartile 1 of Δ Ratio of energy consumption between dinner and breakfast, there were more younger participants, more people who were non-Hispanic white, more current smokers, and current drinkers in quartile 4, who had higher waist circumference, higher energy consumption at dinner, and higher weight, but had lower energy consumption at breakfast, lower dietary fiber, and lower dietary cholesterol. No differences were found in BMI, regular exercise, the medicine used for hypertension, and the medicine used for cholesterol across these quartiles. Table 1 Characteristics in terms of quartiles of ΔRatio of energy consumption between dinner and breakfast: NHANES, 2003–2016 Variables Q1 (N = 6971) Q2 (N = 6981) Q3 (N = 6987) Q4 (N = 6972) P-value Age, years 48.93 (0.37) 47.55 (0.36) 46.89 (0.32) 46.75 (0.36) < 0.001 Female, % 3772 (54.90%) 3533 (51.58%) 3514 (50.10%) 3685 (53.11%) 0.141 Non-Hispanic white, % 2459 (59.52%) 3323 (70.43%) 3620 (73.11%) 3586 (71.96%) < 0.001 Weight (Kg) 80.26 (0.35) 82.59 (0.33) 83.04 (0.39) 82.70 (0.33) < 0.001 BMI, kg/m 2 28.57 (0.14) 28.84 (0.13) 28.79 (0.14) 28.83 (0.12) 0.174 Waist circumstance, cm 97.97 (0.33) 98.88 (0.32) 98.90 (0.36) 98.71 (0.30) 0.002 College graduate or above, % 1253 (23.72%) 1759 (31.27%) 1863 (32.61%) 1665 (29.27%) $ 100,000 annual household income, % 592 (13.56%) 877 (17.72%) 985 (20.41%) 864 (17.01%) < 0.001 Regular exercise, % 1458 (25.12%) 1602 (26.47%) 1674 (27.17%) 1619 (25.86%) 0.205 Current smoker, % 1347 (20.89%) 1573 (22.87%) 1619 (22.48%) 1801 (27.05%) 0.012 Current drinker, % 4456 (67.78%) 4739 (73.09%) 4963 (75.58%) 4860 (74.41%) 0.003 Medicine use for lower blood sugar, % 823 (8.45%) 666 (6.89%) 615 (6.54%) 579 (5.36%) 0.081 Medicine use for hypertension, % 2047 (23.57%) 1971 (23.48%) 1829 (22.17%) 1905 (22.92%) 0.027 Medicine use for cholesterol, % 1429 (17.08%) 1334 (16.85%) 1269 (15.99%) 1294 (17.10%) 0.113 Total energy, kj/day 8242.44 (48.27) 8974.39 (47.73) 9047.90 (48.36) 8120.46 (45.72) < 0.001 Energy at breakfast, kj/day 2775.30 (23.50) 1738.47 (15.05) 1283.83 (15.46) 856.56 (13.53) < 0.001 Energy at dinner, kj/day 1354.34 (19.88) 2356.47 (16.91) 3425.89 (22.10) 5185.99 (35.05) < 0.001 Total fat, g/day 73.77 (0.62) 81.43 (0.57) 83.20 (0.67) 74.41 (0.62) < 0.001 Total protein, g/day 77.36 (0.62) 84.17 (0.60) 84.84 (0.57) 77.33 (0.62) < 0.001 SFA, g/day 24.00 (0.25) 26.55 (0.22) 27.40 (0.24) 24.28 (0.22) < 0.001 Dietary fiber, g/day 16.73 (0.19) 17.50 (0.16) 17.12 (0.20) 15.25 (0.17) < 0.001 Dietary cholesterol, mg/day 294.97 (3.58) 293.54 (3.22) 287.62 (3.01) 257.54 (3.12) < 0.001 Dietary supplements use, % 3503 (54.19%) 3671 (55.39%) 3680 (56.51%) 3446 (51.82%) 0.796 AHEI 52.67 (0.23) 56.19 (0.23) 56.63 (0.26) 52.56 (0.23) < 0.001 General obesity, % 2168 (29.20%) 2134 (29.62%) 2137 (29.98%) 2172 (28.76%) 0.932 Morbid obesity, % 374 (5.24%) 462 (6.32%) 500 (6.24%) 527 (6.97%) 0.001 Abdominal obesity, % 4072 (55.15%) 3946 (55.56%) 3965 (55.05%) 4024 (56.08%) 0.058 Percentage(%) energy from carbohydrate 49.75 (0.18) 48.75 (0.17) 47.96 (0.17) 47.57 (0.15) < 0.001 Percentage(%) energy from fat 33.95 (0.13) 33.95 (0.12) 34.53 (0.14) 34.35 (0.14) < 0.001 Percentage(%) energy from protein 15.95 (0.08) 15.95 (0.07) 15.97 (0.07) 16.21 (0.06) 0.019 Ratio of High-quality carbohydrates at breakfast 0.27 (0.01) 0.22 (0.01) 0.20 (0.01) 0.16 (0.01) < 0.001 Ratio of High-quality carbohydrates at dinner 0.31 (0.01) 0.35 (0.01) 0.40 (0.01) 0.48 (0.01) < 0.001 Ratio of Low-quality carbohydrates at breakfast 0.30 (0.01) 0.22 (0.01) 0.19 (0.01) 0.16 (0.01) < 0.001 Ratio of Low-quality carbohydrates at dinner 0.22 (0.01) 0.25 (0.01) 0.30 (0.01) 0.41 (0.01) < 0.001 Ratio of animal protein at breakfast 0.23 (0.01) 0.14 (0.01) 0.11 (0.01) 0.09 (0.01) < 0.001 Ratio of animal protein at dinner 0.42 (0.01) 0.47 (0.01) 0.53 (0.01) 0.63 (0.01) < 0.001 Ratio of plant protein at breakfast 0.33 (0.01) 0.24 (0.01) 0.20 (0.01) 0.17 (0.01) < 0.001 Ratio of plant protein at dinner 0.27 (0.01) 0.31 (0.01) 0.36 (0.01) 0.47 (0.01) < 0.001 All data analyses in the present study were based on weighted estimates with sample weights provided by the NHANES. Continuous variables were presented as mean (standard error). Categorical variables were presented as n (%). P values were calculated by general linear model for all variables adjusting for age. BMI, body mass index (kg/m 2 ); SFA, saturated fatty acid; AHEI, Alternative Healthy Eating Index; Q, quartile. Association of ΔRatio of consumption of energy and macronutrients, ΔRatio at breakfast, and ΔRatio at dinner with general obesity, morbid obesity, and abdominal obesity Association of ΔRatio of energy and macronutrients with general obesity, morbid obesity, and abdominal obesity is shown in Table 2 . After the adjustment of a variety of covariates, the highest quartile of ΔRatio of low-quality carbohydrates (higher low-quality carbohydrate consumption at dinner than that at breakfast) was significantly positively associated with abdominal obesity (OR 1.16; 95%CI 1.03–1.31). Even though P for trend was not significant, the highest quartile of ΔRatio of energy (higher energy consumption at dinner than that at breakfast) (OR 1.27; 95%CI 1.01–1.61), ΔRatio of fat (higher fat consumption at dinner than that at breakfast) (OR 1.27; 95%CI 1.01–1.60), ΔRatio of SFA (higher SFA consumption at dinner than that at breakfast) (OR 1.27; 95%CI 1.01–1.59), and ΔRatio of USFA (higher USFA consumption at dinner than that at breakfast) (OR 1.28; 95%CI 1.02–1.59) showed that there were higher odds for these people to have obesity. No significant association was found for ΔRatio of protein. Table 2 Association of Δ Ratio of consumption of energy and macronutrients with general obesity, morbid obesity, and abdominal obesity Δ Ratio General obesity Morbid obesity Abdominal obesity Energy Q1 (-1.78 to -0.02) 1.00 (ref) 1.00 (ref) 1.00 (ref) Q2 (-0.02 to 0.15) 1.05 (0.94;1.17) 1.19 (0.96;1.48) 1.12 (1.01;1.25) Q3 (0.15 to 0.33) 1.06 (0.95;1.19) 1.16 (0.91;1.48) 1.11 (0.98;1.26) Q4 (0.33 to 2.00) 0.97 (0.86;1.11) 1.27 (1.01;1.61) 1.11 (0.97;1.27) P for trend 0.666 0.059 0.181 Carbohydrate Q1 (-1.87 to -0.09) 1.00 (ref) 1.00 (ref) 1.00 (ref) Q2 (-0.09 to 0.09) 1.08 (0.98;1.20) 0.87 (0.67;1.12) 1.05 (0.93;1.17) Q3 (0.09 to 0.27) 1.02 (0.91;1.14) 1.08 (0.85;1.36) 1.05 (0.93;1.19) Q4 (0.27 to 2.00) 1.07 (0.95;1.21) 0.97 (0.76;1.24) 1.12 (0.98;1.28) P for trend 0.402 0.732 0.094 High-quality carbohydrate Q1 (-2.00 to -0.12) 1.00 (ref) 1.00 (ref) 1.00 (ref) Q2 (-0.12 to 0.12) 1.02 (0.90;1.16) 1.16 (0.91;1.46) 1.00 (0.90;1.13) Q3 (0.12 to 0.40) 0.93 (0.83;1.05) 1.02 (0.84;1.24) 0.97 (0.86;1.10) Q4 (0.40 to 2.00) 1.08 (0.96;1.22) 1.13 (0.92;1.37) 1.08 (0.95;1.22) P for trend 0.300 0.436 0.246 Low-quality carbohydrate Q1 (-2.00 to -0.10) 1.00 (ref) 1.00 (ref) 1.00 (ref) Q2 (-0.10 to 0.07) 0.94 (0.83;1.06) 0.94 (0.76;1.15) 0.98 (0.87;1.09) Q3 (0.07 to 0.26) 1.04 (0.92;1.19) 0.81 (0.66;1.01) 1.07 (0.95;1.21) Q4 (0.26 to 2.00) 1.06 (0.93;1.20) 1.04 (0.82;1.31) 1.16 (1.03;1.31) P for trend 0.180 0.847 0.004 Fat Q1 (-2.00 to -0.02) 1.00 (ref) 1.00 (ref) 1.00 (ref) Q2 (-0.02 to 0.18) 0.97 (0.88;1.08) 1.06 (0.85;1.32) 1.12 (1.00;1.24) Q3 (0.18 to 0.41) 1.07 (0.96;1.20) 1.04 (0.82;1.32) 1.12 (1.00;1.26) Q4 (0.41 to 2.00) 0.93 (0.82;1.07) 1.27 (1.01;1.60) 1.12 (0.98;1.29) P for trend 0.517 0.044 0.121 SFA Q1 (-2.00 to -0.05) 1.00 (ref) 1.00 (ref) 1.00 (ref) Q2 (-0.05 to 0.16) 0.95 (0.86;1.05) 1.03 (0.81;1.31) 1.08 (0.96;1.22) Q3 (0.16 to 0.40) 1.06 (0.96;1.17) 1.07 (0.84;1.35) 1.12 (1.00;1.25) Q4 (0.40 to 2.00) 0.90 (0.80;1.02) 1.27 (1.01;1.59) 1.12 (0.99;1.26) P for trend 0.259 0.017 0.072 USFA Q1 (-2.00 to -0.02) 1.00 (ref) 1.00 (ref) 1.00 (ref) Q2 (-0.02 to 0.19) 0.99 (0.90;1.09) 1.12 (0.87;1.45) 1.09 (0.97;1.23) Q3 (0.19 to 0.42) 0.99 (0.89;1.11) 1.05 (0.81;1.34) 1.07 (0.96;1.19) Q4 (0.42 to 2.00) 0.93 (0.82;1.06) 1.28 (1.02;1.59) 1.08 (0.95;1.23) P for trend 0.289 0.037 0.300 Protein Q1 (-2.00 to 0.02) 1.00 (ref) 1.00 (ref) 1.00 (ref) Q2 (0.02 to 0.23) 1.09 (0.97;1.22) 1.22 (0.97;1.55) 1.09 (0.97;1.23) Q3 (0.23 to 0.46) 1.03 (0.91;1.16) 1.43 (1.15;1.77) 1.11 (0.99;1.24) Q4 (0.46 to 2.00) 1.02 (0.90;1.16) 1.23 (0.97;1.55) 1.10 (0.97;1.25) P for trend 0.978 0.077 0.177 Animal protein Q1 (-2.00 to 0.13) 1.00 (ref) 1.00 (ref) 1.00 (ref) Q2 (0.13 to 0.38) 0.97 (0.87;1.09) 1.14 (0.94;1.38) 1.08 (0.96;1.22) Q3 (0.38 to 0.59) 0.99 (0.88;1.10) 1.17 (0.96;1.43) 1.06 (0.94;1.20) Q4 (0.59 to 2.00) 0.92 (0.82;1.03) 0.98 (0.81;1.19) 1.02 (0.90;1.16) P for trend 0.185 0.934 0.842 Plant protein Q1 (-2.00 to 0.10) 1.00 (ref) 1.00 (ref) 1.00 (ref) Q2 (0.10 to 0.10) 0.93 (0.84;1.04) 1.01 (0.82;1.25) 1.04 (0.93;1.17) Q3 (0.10 to 0.32) 0.92 (0.82;1.04) 0.94 (0.73;1.19) 1.02 (0.91;1.15) Q4 (0.32 to 2.00) 0.91 (0.82;1.02) 1.09 (0.89;1.34) 1.08 (0.97;1.21) P for trend 0.152 0.458 0.190 Adjustments included age, sex, ethnicity, income, education, regular exercise, smoke, alcohol intake, supplement use, medication use for lower blood sugar, medication use for hypertension, medication use for cholesterol, and total intake of energy, fat, protein, SFA, high-quality carbohydrate, animal protein, dietary fiber, dietary cholesterol, AHEI and breakfast skipping; SFA, saturated fatty acid; USFA,unsaturated fatty acid; AHEI, Alternative Healthy Eating Index; Q, quartile. The association of energy and macronutrient consumption at breakfast with general obesity, morbid obesity, and abdominal obesity are shown in Supplementary Table 2. As indicated by ORs and 95%CIs, the highest quartile of high-quality carbohydrate consumption at breakfast (higher high-quality carbohydrate consumption at breakfast) was related to lower odds of general obesity (OR 0.89; 95%CI 0.80-1.00) and abdominal obesity (OR 0.87; 95%CI 0.77–0.98). Moreover, the highest quartile of low-quality carbohydrate consumption at breakfast (higher low-quality carbohydrate consumption at breakfast) was related to lower odds of abdominal obesity (OR 0.88; 95%CI 0.78-1.00). The association of energy and macronutrient consumption at dinner with general obesity, morbid obesity, and abdominal obesity was shown in Supplementary Table 3. The results reveal that the highest quartile of fat and SFA consumption at dinner (higher fat and SFA consumption at dinner than that at breakfast) was positively associated with morbid obesity ((OR fat 1.28; 95%CI 1.01–1.63) ; (OR SFA 1.26; 95%CI 1.01–1.56)). Association of ΔRatio of energy and macronutrients with BMI The association of ΔRatio of energy and macronutrients with BMI is shown in Table 3 . After the adjustment of a variety of covariates, ΔRatio of low-quality carbohydrates (higher low-quality carbohydrate consumption at dinner than that at breakfast) was significantly positively associated with higher BMI (coefficient: 0.562, 95%CI: 0.217–0.907). No linear trend was observed for ΔRatio of energy and other macronutrients. Table 3 Association of Δ Ratio of energy and macronutrients with BMI. Δ Ratio Coefficient (95% CI) P -value Energy 0.145 (-0.175;0.466) 0.371 Carbohydrate 0.074 (-0.223;0.370) 0.624 High-quality carbohydrate -0.022 (-0.253;0.208) 0.848 Low-quality carbohydrate 0.562 (0.217;0.907) 0.002 Fat 0.138 (-0.133;0.409) 0.314 SFA 0.071 (-0.139;0.281) 0.505 USFA 0.127 (-0.151;0.405) 0.366 Protein 0.019 (-0.220;0.258) 0.875 Animal protein 0.027 (-0.223;0.276) 0.833 Plant protein 0.144 (-0.165;0.453) 0.357 Adjustments included age, sex, ethnicity, income, education, exercise, smoke, alcohol intake, supplement use, medication use for lower blood sugar, medication use for hypertension, medication use for cholesterol, and total intake of energy, fat, protein, SFA, high-quality carbohydrate, animal protein, dietary fiber, dietary cholesterol, AHEI, breakfast skipping; Q, quartile. SFA, saturated fatty acid; USFA,unsaturated fatty acid; AHEI, Alternative Healthy Eating Index. Association of ΔFoods, foods at breakfast, and foods at dinner with general obesity, morbid obesity, and abdominal obesity Figure 1 shows the association of ΔFoods with general obesity, morbid obesity, and abdominal obesity. For high-quality carbohydrate foods, ΔWhole fruits (consumption of 1 more cup-equivalent at dinner than that at breakfast) were positively associated with abdominal obesity (OR 1.08; 95%CI 1.01–1.17). For low-quality carbohydrate foods, ΔOther starchy vegetables (consumption of 1 more cup-equivalent at dinner than that at breakfast) were positively associated with general obesity (OR 1.41; 95%CI 1.02–1.95) and abdominal obesity (OR 1.53; 95%CI 1.10–2.15), respectively. Meanwhile, ΔAdded sugars (consumption of 1 more tsp-equivalent at dinner than that at breakfast) were positively associated with abdominal obesity (OR 1.01; 95%CI 1.00-1.02). Similarly, ΔFoods of animal protein including poultry (consumption of 1 more ounce-equivalent at dinner than that at breakfast) and dairy (consumption of 1 more ounce-equivalent at dinner than that at breakfast) were positively associated with abdominal obesity (OR ΔPoultry 1.04, 95%CI 1.01–1.08; OR ΔDairy 1.07, 95%CI 1.02–1.12). ΔFoods of plant protein including nuts (consumption of 1 more ounce-equivalent at dinner than breakfast) were positively associated with general obesity (OR 1.06; 95%CI 1.00-1.14) and abdominal obesity (OR 1.06; 95%CI 1.00-1.13), respectively. The association of food consumption at breakfast with general obesity, morbid obesity, and abdominal obesity are shown in Supplementary Fig. 4. High-quality carbohydrates of whole fruits (per cup-equivalent) consumption at breakfast was related to lower odds of abdominal obesity (OR 0.82; 95%CI 0.73–0.94). For animal protein foods, poultry (per ounce-equivalent) consumption at breakfast was negatively associated with general obesity (OR 0.89; 95%CI 0.81–0.98) and abdominal obesity (OR 0.88; 95%CI 0.82–0.96). Dairy (per ounce-equivalent) consumption at breakfast was related to lower odds of abdominal obesity (OR 0.88; 95%CI 0.81–0.96). Meanwhile, plant protein foods of nuts (per ounce-equivalent) consumption at breakfast were associated with lower odds of abdominal obesity (OR 0.88; 95%CI 0.79–0.99). No association was observed for low-quality carbohydrate foods. The association of food consumption at dinner with general obesity, morbid obesity, and abdominal obesity are shown in Supplementary Fig. 5. Low-quality carbohydrate of other starchy vegetables (per cup-equivalent) consumption at dinner was significantly positively associated with general obesity (OR 1.76; 95%CI 1.22–2.54) and abdominal obesity (OR 1.76; 95%CI 1.12–2.75), respectively, while, added sugars (per tsp-equivalent) consumption at dinner was positive associated with morbid obesity (OR 1.02; 95%CI 1.00-1.04). For animal protein foods, poultry (per ounce-equivalent) consumption at dinner was associated with higher odds of abdominal obesity (OR 1.04; 95%CI 1.00-1.08). No significant association was observed between high-quality carbohydrates and plant protein foods with obesity. Association of replacing 1 unit food consumption at dinner with the equivalent unit of food consumption at breakfast with general obesity, morbid obesity, and abdominal obesity Figure 2 shows the associations of decreasing the consumption of 1 unit of food at dinner and simultaneously increasing the consumption of 1 unit of food at breakfast with general obesity, morbid obesity, and abdominal obesity. Overall, it was found that a cup-equivalent decrease in whole fruit consumption at dinner with a cup-equivalent increase at breakfast was negatively associated with abdominal obesity (OR 0.88; 95%CI 0.77-1.00). A cup-equivalent decrease in non-starchy vegetable consumption at dinner with a cup equivalent increase at breakfast was negatively associated with general obesity (OR 0.87; 95%CI 0.77–0.98). In addition, a cup-equivalent decrease in other starchy vegetable consumption at dinner with a cup-equivalent increase at breakfast was related to morbid obesity (OR 0.28; 95%CI 0.09–0.88) and abdominal obesity (OR 0.52; 95%CI 0.27-1.00). The negative substitution associations of a tsp-equivalent of added sugars with general obesity (OR 0.99; 95%CI 0.98-1.00) and abdominal obesity (OR 0.98; 95%CI 0.97–0.99) were also observed. Similarly, the substitution of an ounce-equivalent of poultry was negatively associated with general obesity (OR 0.89; 95%CI 0.83–0.95) and abdominal obesity (OR 0.87; 95%CI 0.82–0.93), respectively. The substitution of an ounce-equivalent of dairy was negatively associated with abdominal obesity (OR 0.87; 95%CI 0.80–0.94). Furthermore, the substitution of an ounce-equivalent of nuts was negatively associated with general obesity (OR 0.87; 95%CI 0.77–0.97) and abdominal obesity (OR 0.88; 95%CI 0.79–0.98). Sensitivity analyses After the exclusion of participants with breakfast skipping(Supplementary Table 4) and dinner skipping(Supplementary Table 5), the association of ΔRatio of energy and macronutrients with outcomes was consistent with those from the primary analyses of the complete sample of participants. Meanwhile, similar results were observed when the snack consumption between breakfast and lunch as well as snack after dinner (Δ = (dinner + snack after dinner) – (breakfast + snack between breakfast and lunch))(Supplementary Table 6) was considered in an energy-adjusted form (Supplementary Table 7). After the stratification with total energy consumption throughout the day into tertiles, ΔRatio of fat was negatively associated with general obesity (OR 0.82; 95% CI 0.68–0.99) among participants with the lowest total energy consumption in tertile 1, the possible reason of which was the low total energy consumption (Supplementary Table 8–10). The results were consistent with those from the primary analyses of the complete sample of participants with the exclusion of shift workers in 2005–2010 (Supplementary Table 11) and additionally adjusted with sleep hours in 2005–2016 (Supplementary Table 12). As shown in Supplementary Table 13, when the adjustment was made according to the participants who did not consume both breakfast and snack after breakfast, results were still consistent with those from the primary analyses. The results were consistent with the primary results in Supplementary Table 14 and Supplementary Table 15 without excluding participants with extreme energy intake and additionally adjusted with timing for breakfast and dinner, which suggest that the exclusion of these individuals, timing for breakfast and dinner did not influence the results. Discussion With this nationally representative sample of U.S adults, this study demonstrated that excessive ratio of energy, low-quality carbohydrates, fat, SFA, and USFA consumption at dinner versus breakfast during a day was associated with obesity-related outcomes. A higher intake of low-quality carbohydrates at dinner than that at breakfast was also associated with higher BMI. Higher intake of foods including whole fruits, other-starchy vegetables, added sugars, poultry, dairy, and nuts at dinner than that at breakfast was associated with a higher odds of obesity-related outcomes. Furthermore, replacing 1 unit of whole fruits, non-starchy vegetables, other starchy vegetables, added sugars, poultry, dairy, and nuts consumption at dinner with the same unit of consumption at breakfast had lower odds of having obesity. Overall, this study illustrated that obesity indexes vary with meal patterns and quality of macronutrients irrespective of the total energy intake. To the best of our knowledge, this is the first study to report the association of energy intake, variety quality of macronutrients, and foods distribution during the day with obesity-related outcomes based on nationally representative data. Cross-sectional and cohort studies showed that the body weight of breakfast skippers or light breakfast eaters had increased [ 35 – 38 ], however, experimental evidence had been lacking to substantiate these observations [ 39 , 40 ]. Most experimental studies conducted stringent dietary interventions, which cannot reflect the relationship between actual dietary intake and obesity. Late-night eating has been associated with obesity in several cross-sectional studies [ 5 , 7 ], which was consistent with the results of this study. Two randomized clinical trials on women with obesity [ 41 ] or women with polycystic ovary syndrome [ 42 ] showed better glucose tolerance when participants had 50–54% calories at breakfast rather than having them at dinner. In the large breakfast groups of both studies, fasting glucose was reduced by 7–8%, fasting insulin was reduced by 22–53%, glucose Area Under Curve (AUC) was reduced by 7–20%, insulin AUC was reduced by 28–42%, with higher insulin sensitivity indices. Meanwhile, multiple studies have reported that high-energy breakfast consumption was associated with body weight reduction [ 43 , 44 ] and a lower average BMI [ 45 ]. Animal studies have also demonstrated diurnal rhythms in energy metabolism both at the molecular and whole-body levels [ 46 – 48 ]. Moreover, this study also found that a higher intake of foods including whole fruits, other-starchy vegetables, added sugars, poultry, dairy, and nuts at dinner than that at breakfast were associated with obesity-related outcomes. It is worth noting that the consumption of whole fruits and nuts during the day can contribute to the prevention and management of overweight and obesity among adults [ 49 , 50 ]. However, most types of whole fruits have high simple sugar content, such as sucrose, fructose, and glucose [ 51 ]. Whole fruit consumption at dinner was positively associated with abdominal obesity in this study, perhaps due to the overconsumption of simple sugars, which is one of the main causes of obesity and related diseases [ 52 – 54 ]. Furthermore, the overconsumption of high calorie foods of nuts is often accompanied by excessive energy intake at dinner [ 55 ], which had higher odds of obesity in the current study as well. It has been reported that abdominal obesity is significantly associated with cardiovascular disease and diabetes mortality [ 56 ], which can provide a certain theoretical basis for health care. In addition, it was shown that breakfast consumers had less abdominal obesity when their breakfast was composed of fruit, natural cereal flakes, nuts, and yogurt [ 57 ]. Moreover, it was also observed that the ratio of macronutrients including high-quality carbohydrates and low-carbohydrates, as well as consumption of foods including whole fruits, poultry, dairy, and nuts at breakfast were related to a lower odds of having obesity. Evidence suggested that low-quality carbohydrate consumption at breakfast could secret effective insulin and incretins, which may attenuate postprandial glycemic responses and decrease insulin requirements at lunch and dinner [ 58 , 59 ]. Further, the low-quality carbohydrate intake at breakfast could improve glucose tolerance during the day by benefiting the metabolic and incretin systems [ 60 ]. By contrast, the ratio of fat, consumption of foods including other starchy vegetables, and poultry at dinner were related to obesity-related outcomes in the present study. These results proved the converse association of macronutrients and foods with obesity-related outcomes in different meal-timing. Furthermore, replacing 1 unit of the above food consumption at dinner with the same unit of food consumption at breakfast resulted in lower odds of obesity. One possible mechanism could be that meal timing was related to markers of the circadian clock, aging, and autophagy [ 61 ]. Studies have shown that energy distribution throughout a day can affect body weight, blood pressure, inflammation, appetite, insulin sensitivity, and lipid profiles [ 62 – 64 ]. This association was consistent with our primary analyses with further consideration of a series of traditional obesity-related dietary factors [ 65 – 67 ], as well as breakfast skipping, dietary quality, energy consumption status, shift or rotating workers, and sleep hours [ 35 , 68 – 70 ]. After re-inclusion participants with extreme energy intake, the results were still consistent with main results. Even if this small group of participants with extreme energy intake may have no impact on the associations, these participants were still removed in order to ensure the rigor based on previous literature reported [ 67 ]. Further, the results were still robust when additionally consideration of timing for breakfast and dinner, which suggested that the timing for breakfast and dinner have no influence on the current study. Therefore, in dietary counselling, the emphasis should be put on not only the quality of foods, but also the meal-timing. The results of this study suggest that both the quantity of macronutrients and food sources and the energy distribution throughout a day need to be taken into consideration for dietary recommendations to prevent obesity, which provides certain theoretical significance for the establishment of dietary guidelines in the field of public health and suggests that people should attach importance to the quality and meal timing of foods for the prevention of obesity. Strengths and Limitations This study has several strengths. First of all, it was based on the nationally representative data from the well-designed study (NHANES). Second, the association remained robust with the consideration of a series of traditional obesity-related dietary factors [ 65 – 67 ], as well as breakfast skipping, dinner skipping, snack consumption, dietary quality, shift workers, and sleep hours. Third, the association remained robust when the data were stratified by total energy consumption throughout the day and suggested the importance of energy distribution throughout the day. Despite the obtained results, limitations still exist in this study. First, it was a cross-sectional study and could not establish causal inferences. Second, measurement error was unavoidable for self-reported diet and other information, which may result in an overestimation or underestimation of the association. Third, a series of confounders were considered, but residual confounders might remain. Conclusion This study indicated that among US adults, higher intake of energy, macronutrients (low-quality carbohydrates, fat, SFA, and USFA), and foods (whole fruits, other starchy vegetables, added sugars, poultry, dairy, and nuts) at dinner than that at breakfast was associated with higher odds of having obesity. This study also emphasized the importance of diet quality and meal-timing for the prevention of obesity. Abbreviations BMI Body Mass Index WC Waist circumference SFA Saturated fatty acids USFA Unsaturated fatty acids NHANES National Health and Nutrition Examination Survey AHEI Alternative Healthy Eating Index ORs Odds ratios CIs Confidence intervals Declarations Ethics approval and consent to participate The NHANES protocol was approved by the Ethics Review Board of the National Center for Health Statistics Research, and all participants provided informed consent. Consent for publication Not applicable. Competing interests The authors declare no other conflict of interest. Funding This research was supported by funds from the National Natural Science Foundation of China (82073534 to Changhao Sun). Acknowledgments The authors thank the participants and staff of the National Health and Nutrition Examination Survey 2003–2016 for valuable contributions. Author Contributions All authors made a significant contribution to this article. CH.S planned the work. WY.H carried out the statistical analysis. WWQ wrote and reported the work. All authors critically assessed and reviewed the paper and approved the version to be published. CH.S are responsible for the overall content as guarantors. References Ogden C, Carroll M, Lawman H, Fryar C, Kruszon-Moran D, Kit B, Flegal K. Trends in Obesity Prevalence Among Children and Adolescents in the United States, 1988–1994 Through 2013–2014. JAMA. 2016;315(21):2292–9. Ogden C, Troiano R, Briefel R, Kuczmarski R, Flegal K, Johnson C. Prevalence of overweight among preschool children in the United States, 1971 through 1994. Pediatrics. 1997;99(4):E1. Egger G, Dixon J. Obesity and chronic disease: always offender or often just accomplice? Br J Nutr. 2009;102(8):1238–42. Egger G, Dixon J. Non-nutrient causes of low-grade, systemic inflammation: support for a 'canary in the mineshaft' view of obesity in chronic disease. Obes Rev. 2011;12(5):339–45. Berg C, Lappas G, Wolk A, Strandhagen E, Torén K, Rosengren A, Thelle D, Lissner L. Eating patterns and portion size associated with obesity in a Swedish population. Appetite. 2009;52(1):21–6. Kutsuma A, Nakajima K, Suwa K. Potential Association between Breakfast Skipping and Concomitant Late-Night-Dinner Eating with Metabolic Syndrome and Proteinuria in the Japanese Population. Scientifica (Cairo) 2014, 2014:253581. Wang JB, Patterson RE, Ang A, Emond JA, Shetty N, Arab L. Timing of energy intake during the day is associated with the risk of obesity in adults. J Hum Nutr Diet. 2014;27(Suppl 2):255–62. Baron KG, Reid KJ, Horn LV, Zee PC. Contribution of evening macronutrient intake to total caloric intake and body mass index. Appetite. 2013;60(1):246–51. Asher G, Sassone-Corsi P. Time for food: the intimate interplay between nutrition, metabolism, and the circadian clock. Cell. 2015;161(1):84–92. Jiang P, Turek F. Timing of meals: when is as critical as what and how much. Am J Physiol Endocrinol metabolism. 2017;312(5):E369–80. Turek FW, Joshu C, Kohsaka A, Lin E, Ivanova G, McDearmon E, Laposky A, Losee-Olson S, Easton A, Jensen DR, et al. Obesity and metabolic syndrome in circadian Clock mutant mice. Science. 2005;308(5724):1043–5. Deshmukh-Taskar P, Nicklas T, Radcliffe J, O'Neil C, Liu Y. The relationship of breakfast skipping and type of breakfast consumed with overweight/obesity, abdominal obesity, other cardiometabolic risk factors and the metabolic syndrome in young adults. The National Health and Nutrition Examination Survey (NHANES): 1999–2006. Public Health Nutr. 2013;16(11):2073–82. Horikawa C, Kodama S, Yachi Y, Heianza Y, Hirasawa R, Ibe Y, Saito K, Shimano H, Yamada N, Sone H. Skipping breakfast and prevalence of overweight and obesity in Asian and Pacific regions: a meta-analysis. Prev Med. 2011;53:260–7. Garaulet M, Vera B, Bonnet-Rubio G, Gómez-Abellán P, Lee Y, Ordovás J. Lunch eating predicts weight-loss effectiveness in carriers of the common allele at PERILIPIN1: the ONTIME (Obesity, Nutrigenetics, Timing, Mediterranean) study. Am J Clin Nutr. 2016;104(4):1160–6. Wesnes KA, Pincock C, Richardson D, Helm G, Hails S. Breakfast reduces declines in attention and memory over the morning in schoolchildren. Appetite. 2003;41(3):329–31. Wesnes KA, Pincock C, Scholey A. Breakfast is associated with enhanced cognitive function in schoolchildren. An internet based study. Appetite. 2012;59(3):646–9. Keim N, Van Loan M, Horn W, Barbieri T, Mayclin P. Weight loss is greater with consumption of large morning meals and fat-free mass is preserved with large evening meals in women on a controlled weight reduction regimen. J Nutr. 1997;127(1):75–82. Timlin MT, Pereira MA. Breakfast frequency and quality in the etiology of adult obesity and chronic diseases. Nutr Rev. 2007;65(6 Pt 1):268–81. Ludwig DS, Hu FB, Tappy L, Brand-Miller J. Dietary carbohydrates: role of quality and quantity in chronic disease. BMJ. 2018;361:k2340. Ludwig DS, Willett WC, Volek JS, Neuhouser ML. Dietary fat: From foe to friend? Science. 2018;362(6416):764–70. Reynolds A, Mann J, Cummings J, Winter N, Mete E, Te Morenga L. Carbohydrate quality and human health: a series of systematic reviews and meta-analyses. Lancet. 2019;393(10170):434–45. Johnson C, Paulose-Ram R, Ogden C, Carroll M, Kruszon-Moran D, Dohrmann S, Curtin L. National health and nutrition examination survey: analytic guidelines, 1999–2010. Vital health Stat Ser 2 Data evaluation methods Res 2013(161):1–24. Shan Z, Rehm C, Rogers G, Ruan M, Wang D, Hu F, Mozaffarian D, Zhang F, Bhupathiraju S. Trends in Dietary Carbohydrate, Protein, and Fat Intake and Diet Quality Among US Adults, 1999–2016. JAMA. 2019;322(12):1178–87. Shan R, Duan W, Liu L, Qi J, Gao J, Zhang Y, Du S, Han T, Pang X, Sun C et al. Low-Carbohydrate, High-Protein, High-Fat Diets Rich in Livestock, Poultry and Their Products Predict Impending Risk of Type 2 Diabetes in Chinese Individuals that Exceed Their Calculated Caloric Requirement. Nutrients 2018, 10(1). Shan Z, Rehm CD, Rogers G, Ruan M, Wang DD, Hu FB, Mozaffarian D, Zhang FF, Bhupathiraju SN. Trends in Dietary Carbohydrate, Protein, and Fat Intake and Diet Quality Among US Adults, 1999–2016. JAMA. 2019;322(12):1178–87. Sacks F, Lichtenstein A, Wu J, Appel L, Creager M, Kris-Etherton P, Miller M, Rimm E, Rudel L, Robinson J, et al. Dietary Fats and Cardiovascular Disease: A Presidential Advisory From the American Heart Association. Circulation. 2017;136(3):e1–23. Kant A, Graubard B. 40-year trends in meal and snack eating behaviors of American adults. J Acad Nutr Dietetics. 2015;115(1):50–63. Zipf G, Chiappa M, Porter KS, Ostchega Y, Lewis BG, Dostal J. National health and nutrition examination survey: plan and operations, 1999–2010. Vital Health Stat 1 2013(56). Woon CYL, Piponov H, Schwartz BE, Moretti VM, Schraut NB, Shah RR, Goldstein WM. Total Knee Arthroplasty in Obesity: In-Hospital Outcomes and National Trends. J Arthroplasty. 2016;31(11):2408–14. Ford ES, Maynard LM, Li C. Trends in mean waist circumference and abdominal obesity among US adults, 1999–2012. JAMA. 2014;312(11):1151–3. Wang D, Leung C, Li Y, Ding E, Chiuve S, Hu F, Willett W. Trends in dietary quality among adults in the United States, 1999 through 2010. JAMA Intern Med. 2014;174(10):1587–95. Willett W, Howe G, Kushi L. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr. 1997;65:S1220–8. discussion 1229S-1231S. Zheng Y, Li Y, Satija A, Pan A, Sotos-Prieto M, Rimm E, Willett W, Hu F. Association of changes in red meat consumption with total and cause specific mortality among US women and men: two prospective cohort studies. BMJ. 2019;365:l2110. Zhong VW, Van Horn L, Greenland P, Carnethon MR, Ning H, Wilkins JT, Lloyd-Jones DM, Allen NB. Associations of Processed Meat, Unprocessed Red Meat, Poultry, or Fish Intake With Incident Cardiovascular Disease and All-Cause Mortality. JAMA Intern Med. 2020;180(4):503–12. St-Onge M-P, Ard J, Baskin ML, Chiuve SE, Johnson HM, Kris-Etherton P, Varady K. Meal Timing and Frequency: Implications for Cardiovascular Disease Prevention: A Scientific Statement From the American Heart Association. Circulation 2017, 135(9). Mesas AE, Muñoz-Pareja M, López-García E, Rodríguez-Artalejo F. Selected eating behaviours and excess body weight: a systematic review. Obes Rev. 2012;13(2):106–35. Horikawa C, Kodama S, Yachi Y, Heianza Y, Hirasawa R, Ibe Y, Saito K, Shimano H, Yamada N, Sone H. Skipping breakfast and prevalence of overweight and obesity in Asian and Pacific regions: a meta-analysis. Prev Med. 2011;53(4–5):260–7. Szajewska H, Ruszczynski M. Systematic review demonstrating that breakfast consumption influences body weight outcomes in children and adolescents in Europe. Crit Rev Food Sci Nutr. 2010;50(2):113–9. Chowdhury EA, Richardson JD, Holman GD, Tsintzas K, Thompson D, Betts JA. The causal role of breakfast in energy balance and health: a randomized controlled trial in obese adults. Am J Clin Nutr. 2016;103(3):747–56. Geliebter A, Astbury NM, Aviram-Friedman R, Yahav E, Hashim S. Skipping breakfast leads to weight loss but also elevated cholesterol compared with consuming daily breakfasts of oat porridge or frosted cornflakes in overweight individuals: a randomised controlled trial. J Nutr Sci. 2014;3:e56. Jakubowicz D, Barnea M, Wainstein J, Froy O. High caloric intake at breakfast vs. dinner differentially influences weight loss of overweight and obese women. Obes (Silver Spring). 2013;21(12):2504–12. Jakubowicz D, Barnea M, Wainstein J, Froy O. Effects of caloric intake timing on insulin resistance and hyperandrogenism in lean women with polycystic ovary syndrome. Clin Sci (Lond). 2013;125(9):423–32. St-Onge M, Ard J, Baskin M, Chiuve S, Johnson H, Kris-Etherton P, Varady K. Meal Timing and Frequency: Implications for Cardiovascular Disease Prevention: A Scientific Statement From the American Heart Association. Circulation. 2017;135(9):e96–121. Jakubowicz D, Wainstein J, Ahrén B, Bar-Dayan Y, Landau Z, Rabinovitz H, Froy O. High-energy breakfast with low-energy dinner decreases overall daily hyperglycaemia in type 2 diabetic patients: a randomised clinical trial. Diabetologia. 2015;58(5):912–9. Purslow L, Sandhu M, Forouhi N, Young E, Luben R, Welch A, Khaw K, Bingham S, Wareham N. Energy intake at breakfast and weight change: prospective study of 6,764 middle-aged men and women. Am J Epidemiol. 2008;167(2):188–92. Panda S. Circadian physiology of metabolism. Science. 2016;354(6315):1008–15. McGinnis GR, Young ME. Circadian regulation of metabolic homeostasis: causes and consequences. Nat Sci Sleep. 2016;8:163–80. Kumar Jha P, Challet E, Kalsbeek A. Circadian rhythms in glucose and lipid metabolism in nocturnal and diurnal mammals. Mol Cell Endocrinol. 2015;418(Pt):74–88. Hebden L, O'Leary F, Rangan A, Singgih Lie E, Hirani V, Allman-Farinelli M. Fruit consumption and adiposity status in adults: A systematic review of current evidence. Crit Rev Food Sci Nutr. 2017;57(12):2526–40. Sugizaki CSA, Naves MMV. Potential Prebiotic Properties of Nuts and Edible Seeds and Their Relationship to Obesity. Nutrients 2018, 10(11). Lee J. Sorbitol, Rubus fruit, and misconception. Food Chem. 2015;166:616–22. van Dam RM, Seidell JC. Carbohydrate intake and obesity. Eur J Clin Nutr. 2007;61(Suppl 1):S75–99. Bosy-Westphal A, Müller MJ. Impact of carbohydrates on weight regain. Curr Opin Clin Nutr Metab Care. 2015;18(4):389–94. Buyken AE, Goletzke J, Joslowski G, Felbick A, Cheng G, Herder C, Brand-Miller JC. Association between carbohydrate quality and inflammatory markers: systematic review of observational and interventional studies. Am J Clin Nutr. 2014;99(4):813–33. de Souza RGM, Schincaglia RM, Pimentel GD, Mota JF. Nuts and Human Health Outcomes: A Systematic Review. Nutrients 2017, 9(12). Fang H, Berg E, Cheng X, Shen W. How to best assess abdominal obesity. Curr Opin Clin Nutr Metab Care. 2018;21(5):360–5. Chatelan A, Castetbon K, Pasquier J, Allemann C, Zuber A, Camenzind-Frey E, Zuberbuehler CA, Bochud M. Association between breakfast composition and abdominal obesity in the Swiss adult population eating breakfast regularly. Int J Behav Nutr Phys Act. 2018;15(1):115. Moore MC, Smith MS, Farmer B, Coate KC, Kraft G, Shiota M, Williams PE, Cherrington AD. Morning Hyperinsulinemia Primes the Liver for Glucose Uptake and Glycogen Storage Later in the Day. Diabetes. 2018;67(7):1237–45. Dimitriadis GD, Maratou E, Kountouri A, Board M, Lambadiari V. Regulation of Postabsorptive and Postprandial Glucose Metabolism by Insulin-Dependent and Insulin-Independent Mechanisms: An Integrative Approach. Nutrients 2021, 13(1). Bonuccelli S, Muscelli E, Gastaldelli A, Barsotti E, Astiarraga BD, Holst JJ, Mari A, Ferrannini E. Improved tolerance to sequential glucose loading (Staub-Traugott effect): size and mechanisms. Am J Physiol Endocrinol Metab. 2009;297(2):E532–7. Jamshed H, Beyl RA, Della Manna DL, Yang ES, Ravussin E, Peterson CM. Early Time-Restricted Feeding Improves 24-Hour Glucose Levels and Affects Markers of the Circadian Clock, Aging, and Autophagy in Humans. Nutrients 2019, 11(6). Mattson MP, Longo VD, Harvie M. Impact of intermittent fasting on health and disease processes. Ageing Res Rev. 2017;39:46–58. Tinsley GM, Horne BD. Intermittent fasting and cardiovascular disease: current evidence and unresolved questions. Future Cardiol. 2018;14(1):47–54. Patterson RE, Sears DD. Metabolic Effects of Intermittent Fasting. Annu Rev Nutr. 2017;37:371–93. Han T, Gao J, Wang L, Li C, Qi L, Sun C, Li Y. The Association of Energy and Macronutrient Intake at Dinner Versus Breakfast With Disease-Specific and All-Cause Mortality Among People With Diabetes: The U.S. National Health and Nutrition Examination Survey, 2003–2014. Diabetes Care. 2020;43(7):1442–8. Shan Z, Guo Y, Hu FB, Liu L, Qi Q. Association of Low-Carbohydrate and Low-Fat Diets With Mortality Among US Adults. JAMA Intern Med. 2020;180(4):513–23. Zheng Y, Li Y, Satija A, Pan A, Sotos-Prieto M, Rimm E, Willett WC, Hu FB. Association of changes in red meat consumption with total and cause specific mortality among US women and men: two prospective cohort studies. BMJ. 2019;365:l2110. Haslam DW, James WPT. Obesity. Lancet. 2005;366(9492):1197–209. Ma X, Chen Q, Pu Y, Guo M, Jiang Z, Huang W, Long Y, Xu Y. Skipping breakfast is associated with overweight and obesity: A systematic review and meta-analysis. Obes Res Clin Pract. 2020;14(1):1–8. Monzani A, Ricotti R, Caputo M, Solito A, Archero F, Bellone S, Prodam F. A Systematic Review of the Association of Skipping Breakfast with Weight and Cardiometabolic Risk Factors in Children and Adolescents. What Should We Better Investigate in the Future? Nutrients 2019, 11(2). Additional Declarations No competing interests reported. Supplementary Files Supplementaryinformation.docx SupplementaryFigure1.pdf SupplementaryFigure2.pdf SupplementaryFigure3.pdf SupplementaryFigure4.pdf SupplementaryFigure5.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4543116","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312586970,"identity":"815112e5-e501-45d4-a0ea-487f6268be4b","order_by":0,"name":"wanying hou","email":"","orcid":"","institution":"Department of Nutrition and Food Hygiene, School of Public Health, Key Laboratory of Precision nutrition and health, Ministry of Education, Harbin Medical University, Heilongjiang, China","correspondingAuthor":false,"prefix":"","firstName":"wanying","middleName":"","lastName":"hou","suffix":""},{"id":312586971,"identity":"b4b47725-9506-47a3-9c81-ef1723c3617c","order_by":1,"name":"weiqi wang","email":"","orcid":"","institution":"National Key Discipline, Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, 157 Baojian Road, Harbin, P. R. China","correspondingAuthor":false,"prefix":"","firstName":"weiqi","middleName":"","lastName":"wang","suffix":""},{"id":312586972,"identity":"8fc09b31-9b41-4c52-8fde-0dd8c7c42f9f","order_by":2,"name":"changhao sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYDACCSBmbAAzGR8kGNjIsbG3HyBaC7PBh4o0Yz6eMwlEa2GTnHHmcOI8CQcDvDr4Zzc/e/h1h52c/IzcA9K8bczpbRIMCQw/KrbhtuTOMXNj2TPJxgY38hKMedvYctukGw8w9py5jVOLgUSCmbRkG3PiBokcg2TeNp7cNpkDCcyMbfi0pH8DaqlPnD8jx+Awb5tEOptEggEBLTlmkh/bDic23MgxbJxxxiCBoBaJGzll0oxtx40NzrwxZvhQkWDYBgzkg/j8wj8jfZvkz7ZqOfn2HPMfCQb/5eXb2w8++FGBWwsIMPOgixzAqx4IGH8QUjEKRsEoGAUjGwAAS9NYG6whMf4AAAAASUVORK5CYII=","orcid":"","institution":"National Key Discipline, Department of Nutrition and Food Hygiene, School of Public Health, Harbin Medical University, 157 Baojian Road, Harbin, P. R. China","correspondingAuthor":true,"prefix":"","firstName":"changhao","middleName":"","lastName":"sun","suffix":""}],"badges":[],"createdAt":"2024-06-07 03:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4543116/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4543116/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59217152,"identity":"b56567a0-ac11-4952-8b2c-ad5f821c48e6","added_by":"auto","created_at":"2024-06-27 19:10:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":210169,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe association of ΔFoods with general obesity, morbid obesity, and abdominal obesity.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4543116/v1/ad1df2b566d4f81468fe4f49.png"},{"id":59217149,"identity":"b497901d-47fe-4947-9925-b7bedb7747ae","added_by":"auto","created_at":"2024-06-27 19:10:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":193320,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of replacing 1 unit food consumption at dinner with the equivalent unit of food consumption at breakfast with general obesity, morbid obesity, and abdominal obesity.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4543116/v1/2e2f46c8acc6f77b4c630761.png"},{"id":64016412,"identity":"8fa1af13-4cab-4887-b717-e8cd02a34ef7","added_by":"auto","created_at":"2024-09-05 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Evidence suggested that high energy consumption was a relevant aspect of nutrition and maybe a major reason for obesity [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, limited studies were conducted on the association of meal timing of energy, macronutrients, and food sources distribution during a day with obesity.\u003c/p\u003e \u003cp\u003ePrevious studies showed that energy distribution had an effect on obesity through disrupting clock gene expression [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and the circadian clock gene played a critical role in energy balance for mammals [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Meanwhile, breakfast skipping [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], late lunch [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and high energy intake at dinner [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] were associated with a higher risk of obesity, as well as lower overall diet quality and poorer cognitive performance [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Researchers reported that breakfast skipping was related to weight gain as a result of overcompensating energy-dense high-fat foods later in the day [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. On the other hand, observational studies reported that breakfast consumption was inversely associated with obesity and chronic disease through the regulation of glycemia, insulinemia, lipid metabolism, and possible appetite [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Meal timing can also be employed to prevent obesity and other metabolic pathologies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Beyond the quantity, limited research was conducted on the quality and food sources of macronutrients at dinner versus breakfast among adults. Evidence suggested that quality and food sources of macronutrients were also closely related to human health [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the current study, we aimed to examine the association of the difference of dietary consumption (energy, macronutrients, and foods) at dinner versus breakfast with obesity among adults, and the data were collected from the National Health and Nutrition Examination Survey (NHANES, 2003\u0026ndash;2016).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe NHANES is a stratified and multistage study with a nationally representative sample of the population of the U.S [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Detailed information was previously provided [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A total of 71058 participants were included from 2003\u0026ndash;2004 to 2015\u0026ndash;2016 in NHANES. This study excluded participants without dietary data during the 7 cycles (N\u0026thinsp;=\u0026thinsp;38838) and adults who were less than 20 years old (N\u0026thinsp;=\u0026thinsp;2222). The participants with extreme energy consumption [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] (\u0026lt;\u0026thinsp;500kcal/day or \u0026gt;\u0026thinsp;3,500kcal/day for women and \u0026lt;\u0026thinsp;800/day or \u0026gt;\u0026thinsp;4,200 kcal/day for men) (N\u0026thinsp;=\u0026thinsp;1111), pregnant women (N\u0026thinsp;=\u0026thinsp;609), and those with missing value on current drinking, current smoking, and BMI were excluded in this study (N\u0026thinsp;=\u0026thinsp;367). At last, a total of 27911 participants were included. The flow chart of the screening process for selecting eligible participants is shown in Supplementary Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e The NHANES protocol was approved by the Ethics Review Board of the National Center for Health Statistics Research, and all participants provided informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDietary assessment\u003c/h2\u003e \u003cp\u003eParticipants\u0026rsquo; food intakes for two nonconsecutive days were collected through 24-hour dietary recall interviews. The first 24-hour dietary recall was conducted in person, and the second one was conducted 3\u0026ndash;10 days later via telephone. Dietary nutrients and energy consumption were estimated with the USDA\u0026rsquo;s Food and Nutrient Database for Dietary Studies (FNDDS), and the mean values of energy, nutrients, and foods consumption for day one and day two of the 24 h dietary recall were used in analyses.\u003c/p\u003e \u003cp\u003eBased on the MyPyramid Equivalents Database 2.0 for USDA Survey Foods (MPED 2.0), the dietary intake component of the NHANES was integrated into 37 MyPyramid major groups and subgroups. Similar kinds of foods were combined into the same group according to the research published on JAMA [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Finally, a total of 4 main food groups and 17 subcategories were analyzed, which were high-quality carbohydrates including whole grains, legumes, whole fruits, and non-starchy vegetables; low-quality carbohydrates including refined grains, fruit juice, potato, other starchy vegetables, and added sugars; animal protein including red meat, processed meat, poultry, marine food, dairy, and eggs; plant protein including whole grains, refined grains, legumes, nuts, and soy. Food sources of fat were not examined for the reason that they are similar to protein food sources, and the existing evidence on fat is mainly focused on the types of fatty acids rather than food sources [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Details of the 4 main food groups were presented in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eParticipants\u0026rsquo; meal timing of breakfast, lunch, and dinner was self-reported. Four snack patterns were determined based on the timing of snacks consumed, including 1) snack between breakfast and lunch, which was the self-reported foods consumption between breakfast and lunch, 2) snack between lunch and dinner, which was the self-reported foods consumption between lunch and dinner, 3) snack after dinner, which was the self-reported foods consumption after dinner, and 4) none of the above, which means that the participants had never consumed foods besides three meals throughout a day [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eMain exposure\u003c/h2\u003e \u003cp\u003eThe primary exposure variable in this study was the difference in the ratio of total energy and macronutrient consumption at dinner versus breakfast throughout the day. For example, the way of calculation was as follows: the ratio of energy at dinner\u0026thinsp;=\u0026thinsp;energy consumption at dinner/total energy; the ratio of energy at breakfast\u0026thinsp;=\u0026thinsp;energy consumption at breakfast/total energy; ΔRatio of energy\u0026thinsp;=\u0026thinsp;the ratio of energy at dinner - ratio of energy at breakfast; The ratio of high-quality carbohydrates at dinner\u0026thinsp;=\u0026thinsp;high-quality carbohydrates consumption at dinner/total high-quality carbohydrates; the ratio of high-quality carbohydrates at breakfast\u0026thinsp;=\u0026thinsp;high-quality carbohydrates at breakfast/total high-quality carbohydrates; ΔRatio of high-quality carbohydrates\u0026thinsp;=\u0026thinsp;the ratio of high-quality carbohydrates at dinner \u0026ndash; the ratio of high-quality carbohydrates at breakfast. The macronutrients examined in this study included carbohydrates (high-quality carbohydrates and low-quality carbohydrates), fat (saturated fatty acids [SFA] and unsaturated fatty acids [USFA, sum of polyunsaturated and monounsaturated fatty acids]), and protein (animal protein and plant protein).\u003c/p\u003e \u003cp\u003eThe second exposure was the difference in 17 subcategories of food consumption between dinner and breakfast, for example, ΔWhole grains\u0026thinsp;=\u0026thinsp;whole grains consumption at dinner \u0026ndash; whole grains consumption at breakfast.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMain outcome\u003c/h2\u003e \u003cp\u003eIn terms of the outcomes, general, morbid, and abdominal obesity were determined by BMI and WC, respectively. Height and weight were measured by standardized procedures [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], in which participants were asked to remove their hair ornaments, jewelry, and other accessories from the head, and whether the back of the head, shoulder blades, buttocks, and heels made contact with the backboard was checked. Participants were weighed in kilograms with a digital weight scale and they should wear a standard MEC (mobile examination center) examination gown, which consists of a disposable shirt, pants, and slippers. At the end of the examination, the weight of participants was displayed in both kilograms and pounds. BMI was calculated by the weight over height squared (kg/m2) and used to define general obesity (30.0\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;40.0) and morbid obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;40.0) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. WC was measured to the nearest 0.1 cm at the high point of the right iliac crest at minimal respiration (cm) and adopted to define abdominal obesity (WC\u0026thinsp;\u0026gt;\u0026thinsp;102 cm for men or WC\u0026thinsp;\u0026gt;\u0026thinsp;88 cm for women) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A further detailed description of examination protocol, quality control, and safety procedures can be found in the Anthropometry Procedures Manual via the NHANES website.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of covariates\u003c/h2\u003e \u003cp\u003ePotential covariates were age (years old), sex (male/female), race/ethnicity (non-Hispanic white/non-Hispanic black/Mexican American/other), education level (\u0026lt;\u0026thinsp;Grade 9/Grade 9\u0026ndash;11/high school graduate/GED or equivalent/some college or Associate degree/college graduate or above), annual household income (\u0026lt; \u003cspan\u003e$\u003c/span\u003e20,000/\u003cspan\u003e$\u003c/span\u003e20,000 - \u003cspan\u003e$\u003c/span\u003e45,000/\u003cspan\u003e$\u003c/span\u003e45,000 - \u003cspan\u003e$\u003c/span\u003e75,000/\u0026gt; \u003cspan\u003e$\u003c/span\u003e100,000), regular exercise (in a typical week, doing some moderate-intensity sports, fitness, or recreational activities that cause a small increase in breathing or heart rate for at least 10 minutes continuously, yes/no), current smoker (yes/no), current drinker (had at least 12 alcohol drinks/1year, yes/no), medicine use for lower blood sugar, medicine use for hypertension, medicine use for cholesterol, total intake of energy (kcal/day), fat (g/day), protein (g/day), SFA (g/day), dietary fiber (g/day), dietary cholesterol (mg/day), high-quality carbohydrates (cups/ounce/tsp equivalent) and animal protein (cups/ounce/tsp equivalent), daily total consumption of foods (cups/ounce/tsp equivalent), dietary supplements use (yes/no), breakfast skipping (energy consumption at breakfast was 0 kcal at the two days\u0026rsquo; 24-hours dietary recall), and diet quality calculated by the Alternative Healthy Eating Index (AHEI) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003e All analyses incorporated the dietary sample weights, stratification, and clustering of the complex sampling design to ensure the nationally representative estimates according to NHANES analytic guidelines. In addition, to correct the measurement error, the absolute intakes of macronutrients and food sources per day were adjusted for total energy intake by the residual method in dietary estimates [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe differences in the ratio of total energy and macronutrient consumption at dinner versus breakfast (ΔRatio\u0026thinsp;=\u0026thinsp;the ratio at dinner \u0026ndash; the ratio at breakfast), the ratio at breakfast, and the ratio at dinner were categorized into quartiles. The differences in food consumption between dinner and breakfast (ΔFoods), foods at breakfast, and foods at dinner were considered as continuous variables. Demographic characteristics, dietary nutrient intake, and anthropometric measurements were presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error for continuous variables and the percentage of categorical variables. The differences of ΔRatio of energy by quartiles were tested with general linear models for all variables after the age adjustment.\u003c/p\u003e \u003cp\u003eMultiple logistic regression models were developed to examine the association of ΔRatio, the ratio at breakfast, the ratio at dinner, ΔFoods, foods at breakfast, foods at dinner, with general obesity, morbid obesity, and abdominal obesity. Odds ratios (ORs) and 95% confidence intervals (CIs) were provided. Categorical variables were modeled as continuous variables through the assignment of the median value to each quartile to test linear trends. Models were adjusted for age, sex, ethnicity, education level, annual household income, regular exercise, current smoker, current drinker, the medicine used for lower blood sugar, the medicine used for hypertension, the medicine used for cholesterol, total intake of energy, fat, protein, SFA, dietary fiber, dietary cholesterol, high-quality carbohydrate, animal protein, dietary supplements use, breakfast skipping, and AHEI. Linear regression models were performed to examine the association of ΔRatio of energy and macronutrients with BMI (BMI was not log-transformed, since it was an approximately normal distribution, as shown in Supplementary Fig.\u0026nbsp;2). For the associations of ΔFood, food at breakfast, and dinner with obesity, models were an additional adjustment for the total daily intake of the food consumption. General linear regression models were also adopted to examine the difference in macronutrients consumption during the day among participants.\u003c/p\u003e \u003cp\u003eThis study further explored the substitution effects on obesity by replacing the food at dinner with 1 equivalent unit of food consumption at breakfast. The food consumption at breakfast and dinner were included in the same multivariable models as continuous variables. For each substitution at dinner with breakfast, the difference between the β coefficients of the 2 variables was adopted to estimate the OR, and the variances and covariance of the 2 variables were adopted to estimate the 95% CI [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The differences in the estimates statistically predicted the substitution effects on the risk of general obesity, morbid obesity, and abdominal obesity. This substitution was interpreted as the association of decreasing 1 equivalent unit of food consumption at dinner and simultaneously increasing the intake at breakfast with obesity. The chart showing all the analyses performed in this study is shown in Supplementary Fig.\u0026nbsp;3.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eSensitivity analyses\u003c/h2\u003e \u003cp\u003eThirteen sensitivity analyses were performed in ΔRatio in terms of energy and macronutrients and obesity with multiple logistic regression models. (1) Participants with breakfast skipping were excluded. (2) Participants with dinner skipping were excluded. (3) The snack consumption between breakfast and lunch as well as snack after dinner were considered (Δ= (dinner ་ snack after dinner) \u0026ndash; (breakfast ་ snack between breakfast and lunch)). (4) Macronutrients in the energy-adjusted form were calculated [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] (macronutrients were measured in units per 1000 kcal per day, and details were shown in Supplementary methods 1). (5) Participants were stratified into tertiles with total energy consumption (tertile1 included participants with lowest energy consumption, tertile2 included participants with middle energy consumption, and tertile3 included participants with highest energy consumption). (6) Participants who were night shift workers or rotating workers from 2005 to 2010 were excluded. (7) Further adjustment was made with sleep hours from 2005 to 2016. (8) Further adjustment was made with participants who did not consume both breakfast and snacks after breakfast. (8) We investigated the above associations among participants without excluding those with extreme energy intake. (9) Additionally adjusted with timing for breakfast and dinner.\u003c/p\u003e \u003cp\u003eAfter Bonferroni correction for multiple comparisons in cox regression models, a two-sided \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 was considered to be statistically significant. All analyses were performed with R 3.6.1.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of participants\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the characteristics among participants aged over 20 from NHANES 2003\u0026ndash;2016 in this study (N\u0026thinsp;=\u0026thinsp;27911). Among the 27911 participants, 8611 had general obesity, 1863 had morbid obesity, and 16007 had abdominal obesity. Compared with participants in quartile 1 of Δ Ratio of energy consumption between dinner and breakfast, there were more younger participants, more people who were non-Hispanic white, more current smokers, and current drinkers in quartile 4, who had higher waist circumference, higher energy consumption at dinner, and higher weight, but had lower energy consumption at breakfast, lower dietary fiber, and lower dietary cholesterol. No differences were found in BMI, regular exercise, the medicine used for hypertension, and the medicine used for cholesterol across these quartiles.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics in terms of quartiles of ΔRatio of energy consumption between dinner and breakfast: NHANES, 2003\u0026ndash;2016\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;6971)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;6981)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;6987)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;6972)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48.93 (0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.55 (0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.89 (0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46.75 (0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3772 (54.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3533 (51.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3514 (50.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3685 (53.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic white, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2459 (59.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3323 (70.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3620 (73.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3586 (71.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (Kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.26 (0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.59 (0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.04 (0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.70 (0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.57 (0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.84 (0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.79 (0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.83 (0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumstance, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97.97 (0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98.88 (0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.90 (0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98.71 (0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege graduate or above, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1253 (23.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1759 (31.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1863 (32.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1665 (29.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u003cspan\u003e$\u003c/span\u003e100,000 annual household income, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e592 (13.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e877 (17.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e985 (20.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e864 (17.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular exercise, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1458 (25.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1602 (26.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1674 (27.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1619 (25.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1347 (20.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1573 (22.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1619 (22.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1801 (27.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent drinker, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4456 (67.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4739 (73.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4963 (75.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4860 (74.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicine use for lower blood sugar, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e823 (8.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e666 (6.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e615 (6.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e579 (5.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicine use for hypertension, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2047 (23.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1971 (23.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1829 (22.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1905 (22.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicine use for cholesterol, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1429 (17.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1334 (16.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1269 (15.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1294 (17.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal energy, kj/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8242.44 (48.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8974.39 (47.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9047.90 (48.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8120.46 (45.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy at breakfast, kj/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2775.30 (23.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1738.47 (15.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1283.83 (15.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e856.56 (13.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy at dinner, kj/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1354.34 (19.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2356.47 (16.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3425.89 (22.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5185.99 (35.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal fat, g/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73.77 (0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.43 (0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83.20 (0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.41 (0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal protein, g/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77.36 (0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84.17 (0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84.84 (0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77.33 (0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSFA, g/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.00 (0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.55 (0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.40 (0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.28 (0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary fiber, g/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.73 (0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.50 (0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.12 (0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.25 (0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary cholesterol, mg/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e294.97 (3.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e293.54 (3.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e287.62 (3.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e257.54 (3.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDietary supplements use, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3503 (54.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3671 (55.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3680 (56.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3446 (51.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAHEI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.67 (0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.19 (0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.63 (0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52.56 (0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral obesity, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2168 (29.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2134 (29.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2137 (29.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2172 (28.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorbid obesity, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e374 (5.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e462 (6.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e500 (6.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e527 (6.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal obesity, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4072 (55.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3946 (55.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3965 (55.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4024 (56.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage(%) energy from carbohydrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.75 (0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.75 (0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.96 (0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.57 (0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage(%) energy from fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.95 (0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.95 (0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.53 (0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.35 (0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage(%) energy from protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.95 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.95 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.97 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.21 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRatio of High-quality carbohydrates at breakfast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.27 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRatio of High-quality carbohydrates at dinner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.31 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.40 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.48 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRatio of Low-quality carbohydrates at breakfast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.30 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRatio of Low-quality carbohydrates at dinner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.22 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.25 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.30 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRatio of animal protein at breakfast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.23 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRatio of animal protein at dinner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.42 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.47 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.53 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.63 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRatio of plant protein at breakfast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.33 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.24 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.17 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRatio of plant protein at dinner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.27 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.47 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAll data analyses in the present study were based on weighted estimates with sample weights provided by the NHANES. Continuous variables were presented as mean (standard error). Categorical variables were presented as n (%). \u003cem\u003eP\u003c/em\u003e values were calculated by general linear model for all variables adjusting for age.\u003c/p\u003e \u003cp\u003eBMI, body mass index (kg/m\u003csup\u003e2\u003c/sup\u003e); SFA, saturated fatty acid; AHEI, Alternative Healthy Eating Index; Q, quartile.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociation of ΔRatio of consumption of energy and macronutrients, ΔRatio at breakfast, and ΔRatio at dinner with general obesity, morbid obesity, and abdominal obesity\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAssociation of ΔRatio of energy and macronutrients with general obesity, morbid obesity, and abdominal obesity is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. After the adjustment of a variety of covariates, the highest quartile of ΔRatio of low-quality carbohydrates (higher low-quality carbohydrate consumption at dinner than that at breakfast) was significantly positively associated with abdominal obesity (OR 1.16; 95%CI 1.03\u0026ndash;1.31). Even though \u003cem\u003eP\u003c/em\u003e \u003csub\u003e\u003cem\u003efor trend\u003c/em\u003e\u003c/sub\u003e was not significant, the highest quartile of ΔRatio of energy (higher energy consumption at dinner than that at breakfast) (OR 1.27; 95%CI 1.01\u0026ndash;1.61), ΔRatio of fat (higher fat consumption at dinner than that at breakfast) (OR 1.27; 95%CI 1.01\u0026ndash;1.60), ΔRatio of SFA (higher SFA consumption at dinner than that at breakfast) (OR 1.27; 95%CI 1.01\u0026ndash;1.59), and ΔRatio of USFA (higher USFA consumption at dinner than that at breakfast) (OR 1.28; 95%CI 1.02\u0026ndash;1.59) showed that there were higher odds for these people to have obesity. No significant association was found for ΔRatio of protein.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of Δ Ratio of consumption of energy and macronutrients with general obesity, morbid obesity, and abdominal obesity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral obesity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMorbid obesity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbdominal obesity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 (-1.78 to -0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2 (-0.02 to 0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05 (0.94;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19 (0.96;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12 (1.01;1.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3 (0.15 to 0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06 (0.95;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16 (0.91;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11 (0.98;1.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 (0.33 to 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.86;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27 (1.01;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11 (0.97;1.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP \u003csub\u003efor trend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbohydrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 (-1.87 to -0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2 (-0.09 to 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08 (0.98;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87 (0.67;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05 (0.93;1.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3 (0.09 to 0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.91;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08 (0.85;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05 (0.93;1.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 (0.27 to 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07 (0.95;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97 (0.76;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12 (0.98;1.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP \u003csub\u003efor trend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-quality carbohydrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 (-2.00 to -0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2 (-0.12 to 0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.90;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16 (0.91;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.90;1.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3 (0.12 to 0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.83;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02 (0.84;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.86;1.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 (0.40 to 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08 (0.96;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13 (0.92;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.95;1.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP \u003csub\u003efor trend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-quality carbohydrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 (-2.00 to -0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2 (-0.10 to 0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94 (0.83;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94 (0.76;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.87;1.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3 (0.07 to 0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 (0.92;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81 (0.66;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07 (0.95;1.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 (0.26 to 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06 (0.93;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04 (0.82;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16 (1.03;1.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP \u003csub\u003efor trend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 (-2.00 to -0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2 (-0.02 to 0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.88;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06 (0.85;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12 (1.00;1.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3 (0.18 to 0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07 (0.96;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04 (0.82;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12 (1.00;1.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 (0.41 to 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.82;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27 (1.01;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12 (0.98;1.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP \u003csub\u003efor trend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 (-2.00 to -0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2 (-0.05 to 0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.86;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 (0.81;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.96;1.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3 (0.16 to 0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06 (0.96;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07 (0.84;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12 (1.00;1.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 (0.40 to 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90 (0.80;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27 (1.01;1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12 (0.99;1.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP \u003csub\u003efor trend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 (-2.00 to -0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2 (-0.02 to 0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.90;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12 (0.87;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 (0.97;1.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3 (0.19 to 0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.89;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05 (0.81;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07 (0.96;1.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 (0.42 to 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.82;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 (1.02;1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.95;1.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP \u003csub\u003efor trend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 (-2.00 to 0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2 (0.02 to 0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09 (0.97;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22 (0.97;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 (0.97;1.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3 (0.23 to 0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.91;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43 (1.15;1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11 (0.99;1.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 (0.46 to 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.90;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23 (0.97;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10 (0.97;1.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP \u003csub\u003efor trend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnimal protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 (-2.00 to 0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2 (0.13 to 0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.87;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14 (0.94;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.96;1.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3 (0.38 to 0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.88;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17 (0.96;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (0.94;1.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 (0.59 to 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.82;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.81;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.90;1.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP \u003csub\u003efor trend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 (-2.00 to 0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (ref)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2 (0.10 to 0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.84;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01 (0.82;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (0.93;1.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3 (0.10 to 0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.82;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94 (0.73;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.91;1.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 (0.32 to 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0.82;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09 (0.89;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.97;1.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP \u003csub\u003efor trend\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAdjustments included age, sex, ethnicity, income, education, regular exercise, smoke, alcohol intake, supplement use, medication use for lower blood sugar, medication use for hypertension, medication use for cholesterol, and total intake of energy, fat, protein, SFA, high-quality carbohydrate, animal protein, dietary fiber, dietary cholesterol, AHEI and breakfast skipping;\u003c/p\u003e \u003cp\u003eSFA, saturated fatty acid; USFA,unsaturated fatty acid; AHEI, Alternative Healthy Eating Index; Q, quartile.\u003c/p\u003e \u003cp\u003eThe association of energy and macronutrient consumption at breakfast with general obesity, morbid obesity, and abdominal obesity are shown in Supplementary Table\u0026nbsp;2. As indicated by ORs and 95%CIs, the highest quartile of high-quality carbohydrate consumption at breakfast (higher high-quality carbohydrate consumption at breakfast) was related to lower odds of general obesity (OR 0.89; 95%CI 0.80-1.00) and abdominal obesity (OR 0.87; 95%CI 0.77\u0026ndash;0.98). Moreover, the highest quartile of low-quality carbohydrate consumption at breakfast (higher low-quality carbohydrate consumption at breakfast) was related to lower odds of abdominal obesity (OR 0.88; 95%CI 0.78-1.00).\u003c/p\u003e \u003cp\u003eThe association of energy and macronutrient consumption at dinner with general obesity, morbid obesity, and abdominal obesity was shown in Supplementary Table\u0026nbsp;3.\u003c/p\u003e \u003cp\u003eThe results reveal that the highest quartile of fat and SFA consumption at dinner (higher fat and SFA consumption at dinner than that at breakfast) was positively associated with morbid obesity ((OR\u003csub\u003efat\u003c/sub\u003e 1.28; 95%CI 1.01\u0026ndash;1.63) ; (OR\u003csub\u003eSFA\u003c/sub\u003e 1.26; 95%CI 1.01\u0026ndash;1.56)).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of ΔRatio of energy and macronutrients with BMI\u003c/h2\u003e \u003cp\u003eThe association of ΔRatio of energy and macronutrients with BMI is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. After the adjustment of a variety of covariates, ΔRatio of low-quality carbohydrates (higher low-quality carbohydrate consumption at dinner than that at breakfast) was significantly positively associated with higher BMI (coefficient: 0.562, 95%CI: 0.217\u0026ndash;0.907). No linear trend was observed for ΔRatio of energy and other macronutrients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of Δ Ratio of energy and macronutrients with BMI.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.145 (-0.175;0.466)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbohydrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.074 (-0.223;0.370)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-quality carbohydrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.022 (-0.253;0.208)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-quality carbohydrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.562 (0.217;0.907)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.138 (-0.133;0.409)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.071 (-0.139;0.281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.127 (-0.151;0.405)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.019 (-0.220;0.258)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnimal protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.027 (-0.223;0.276)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.144 (-0.165;0.453)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAdjustments included age, sex, ethnicity, income, education, exercise, smoke, alcohol intake, supplement use, medication use for lower blood sugar, medication use for hypertension, medication use for cholesterol, and total intake of energy, fat, protein, SFA, high-quality carbohydrate, animal protein, dietary fiber, dietary cholesterol, AHEI, breakfast skipping; Q, quartile.\u003c/p\u003e \u003cp\u003eSFA, saturated fatty acid; USFA,unsaturated fatty acid; AHEI, Alternative Healthy Eating Index.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociation of ΔFoods, foods at breakfast, and foods at dinner with general obesity, morbid obesity, and abdominal obesity\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the association of ΔFoods with general obesity, morbid obesity, and abdominal obesity. For high-quality carbohydrate foods, ΔWhole fruits (consumption of 1 more cup-equivalent at dinner than that at breakfast) were positively associated with abdominal obesity (OR 1.08; 95%CI 1.01\u0026ndash;1.17). For low-quality carbohydrate foods, ΔOther starchy vegetables (consumption of 1 more cup-equivalent at dinner than that at breakfast) were positively associated with general obesity (OR 1.41; 95%CI 1.02\u0026ndash;1.95) and abdominal obesity (OR 1.53; 95%CI 1.10\u0026ndash;2.15), respectively. Meanwhile, ΔAdded sugars (consumption of 1 more tsp-equivalent at dinner than that at breakfast) were positively associated with abdominal obesity (OR 1.01; 95%CI 1.00-1.02). Similarly, ΔFoods of animal protein including poultry (consumption of 1 more ounce-equivalent at dinner than that at breakfast) and dairy (consumption of 1 more ounce-equivalent at dinner than that at breakfast) were positively associated with abdominal obesity (OR\u003csub\u003eΔPoultry\u003c/sub\u003e 1.04, 95%CI 1.01\u0026ndash;1.08; OR\u003csub\u003eΔDairy\u003c/sub\u003e 1.07, 95%CI 1.02\u0026ndash;1.12). ΔFoods of plant protein including nuts (consumption of 1 more ounce-equivalent at dinner than breakfast) were positively associated with general obesity (OR 1.06; 95%CI 1.00-1.14) and abdominal obesity (OR 1.06; 95%CI 1.00-1.13), respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe association of food consumption at breakfast with general obesity, morbid obesity, and abdominal obesity are shown in Supplementary Fig.\u0026nbsp;4. High-quality carbohydrates of whole fruits (per cup-equivalent) consumption at breakfast was related to lower odds of abdominal obesity (OR 0.82; 95%CI 0.73\u0026ndash;0.94). For animal protein foods, poultry (per ounce-equivalent) consumption at breakfast was negatively associated with general obesity (OR 0.89; 95%CI 0.81\u0026ndash;0.98) and abdominal obesity (OR 0.88; 95%CI 0.82\u0026ndash;0.96). Dairy (per ounce-equivalent) consumption at breakfast was related to lower odds of abdominal obesity (OR 0.88; 95%CI 0.81\u0026ndash;0.96). Meanwhile, plant protein foods of nuts (per ounce-equivalent) consumption at breakfast were associated with lower odds of abdominal obesity (OR 0.88; 95%CI 0.79\u0026ndash;0.99). No association was observed for low-quality carbohydrate foods.\u003c/p\u003e \u003cp\u003eThe association of food consumption at dinner with general obesity, morbid obesity, and abdominal obesity are shown in Supplementary Fig.\u0026nbsp;5. Low-quality carbohydrate of other starchy vegetables (per cup-equivalent) consumption at dinner was significantly positively associated with general obesity (OR 1.76; 95%CI 1.22\u0026ndash;2.54) and abdominal obesity (OR 1.76; 95%CI 1.12\u0026ndash;2.75), respectively, while, added sugars (per tsp-equivalent) consumption at dinner was positive associated with morbid obesity (OR 1.02; 95%CI 1.00-1.04). For animal protein foods, poultry (per ounce-equivalent) consumption at dinner was associated with higher odds of abdominal obesity (OR 1.04; 95%CI 1.00-1.08). No significant association was observed between high-quality carbohydrates and plant protein foods with obesity.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociation of replacing 1 unit food consumption at dinner with the equivalent unit of food consumption at breakfast with general obesity, morbid obesity, and abdominal obesity\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the associations of decreasing the consumption of 1 unit of food at dinner and simultaneously increasing the consumption of 1 unit of food at breakfast with general obesity, morbid obesity, and abdominal obesity. Overall, it was found that a cup-equivalent decrease in whole fruit consumption at dinner with a cup-equivalent increase at breakfast was negatively associated with abdominal obesity (OR 0.88; 95%CI 0.77-1.00). A cup-equivalent decrease in non-starchy vegetable consumption at dinner with a cup equivalent increase at breakfast was negatively associated with general obesity (OR 0.87; 95%CI 0.77\u0026ndash;0.98). In addition, a cup-equivalent decrease in other starchy vegetable consumption at dinner with a cup-equivalent increase at breakfast was related to morbid obesity (OR 0.28; 95%CI 0.09\u0026ndash;0.88) and abdominal obesity (OR 0.52; 95%CI 0.27-1.00). The negative substitution associations of a tsp-equivalent of added sugars with general obesity (OR 0.99; 95%CI 0.98-1.00) and abdominal obesity (OR 0.98; 95%CI 0.97\u0026ndash;0.99) were also observed. Similarly, the substitution of an ounce-equivalent of poultry was negatively associated with general obesity (OR 0.89; 95%CI 0.83\u0026ndash;0.95) and abdominal obesity (OR 0.87; 95%CI 0.82\u0026ndash;0.93), respectively. The substitution of an ounce-equivalent of dairy was negatively associated with abdominal obesity (OR 0.87; 95%CI 0.80\u0026ndash;0.94). Furthermore, the substitution of an ounce-equivalent of nuts was negatively associated with general obesity (OR 0.87; 95%CI 0.77\u0026ndash;0.97) and abdominal obesity (OR 0.88; 95%CI 0.79\u0026ndash;0.98).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analyses\u003c/h2\u003e \u003cp\u003eAfter the exclusion of participants with breakfast skipping(Supplementary Table\u0026nbsp;4) and dinner skipping(Supplementary Table\u0026nbsp;5), the association of ΔRatio of energy and macronutrients with outcomes was consistent with those from the primary analyses of the complete sample of participants. Meanwhile, similar results were observed when the snack consumption between breakfast and lunch as well as snack after dinner (Δ = (dinner\u0026thinsp;+\u0026thinsp;snack after dinner) \u0026ndash; (breakfast\u0026thinsp;+\u0026thinsp;snack between breakfast and lunch))(Supplementary Table\u0026nbsp;6) was considered in an energy-adjusted form (Supplementary Table\u0026nbsp;7). After the stratification with total energy consumption throughout the day into tertiles, ΔRatio of fat was negatively associated with general obesity (OR 0.82; 95% CI 0.68\u0026ndash;0.99) among participants with the lowest total energy consumption in tertile 1, the possible reason of which was the low total energy consumption (Supplementary Table\u0026nbsp;8\u0026ndash;10). The results were consistent with those from the primary analyses of the complete sample of participants with the exclusion of shift workers in 2005\u0026ndash;2010 (Supplementary Table\u0026nbsp;11) and additionally adjusted with sleep hours in 2005\u0026ndash;2016 (Supplementary Table\u0026nbsp;12). As shown in Supplementary Table\u0026nbsp;13, when the adjustment was made according to the participants who did not consume both breakfast and snack after breakfast, results were still consistent with those from the primary analyses. The results were consistent with the primary results in Supplementary Table\u0026nbsp;14 and Supplementary Table\u0026nbsp;15 without excluding participants with extreme energy intake and additionally adjusted with timing for breakfast and dinner, which suggest that the exclusion of these individuals, timing for breakfast and dinner did not influence the results.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWith this nationally representative sample of U.S adults, this study demonstrated that excessive ratio of energy, low-quality carbohydrates, fat, SFA, and USFA consumption at dinner versus breakfast during a day was associated with obesity-related outcomes. A higher intake of low-quality carbohydrates at dinner than that at breakfast was also associated with higher BMI. Higher intake of foods including whole fruits, other-starchy vegetables, added sugars, poultry, dairy, and nuts at dinner than that at breakfast was associated with a higher odds of obesity-related outcomes. Furthermore, replacing 1 unit of whole fruits, non-starchy vegetables, other starchy vegetables, added sugars, poultry, dairy, and nuts consumption at dinner with the same unit of consumption at breakfast had lower odds of having obesity. Overall, this study illustrated that obesity indexes vary with meal patterns and quality of macronutrients irrespective of the total energy intake.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this is the first study to report the association of energy intake, variety quality of macronutrients, and foods distribution during the day with obesity-related outcomes based on nationally representative data. Cross-sectional and cohort studies showed that the body weight of breakfast skippers or light breakfast eaters had increased [\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], however, experimental evidence had been lacking to substantiate these observations [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Most experimental studies conducted stringent dietary interventions, which cannot reflect the relationship between actual dietary intake and obesity. Late-night eating has been associated with obesity in several cross-sectional studies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], which was consistent with the results of this study. Two randomized clinical trials on women with obesity [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] or women with polycystic ovary syndrome [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] showed better glucose tolerance when participants had 50\u0026ndash;54% calories at breakfast rather than having them at dinner. In the large breakfast groups of both studies, fasting glucose was reduced by 7\u0026ndash;8%, fasting insulin was reduced by 22\u0026ndash;53%, glucose Area Under Curve (AUC) was reduced by 7\u0026ndash;20%, insulin AUC was reduced by 28\u0026ndash;42%, with higher insulin sensitivity indices. Meanwhile, multiple studies have reported that high-energy breakfast consumption was associated with body weight reduction [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and a lower average BMI [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Animal studies have also demonstrated diurnal rhythms in energy metabolism both at the molecular and whole-body levels [\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Moreover, this study also found that a higher intake of foods including whole fruits, other-starchy vegetables, added sugars, poultry, dairy, and nuts at dinner than that at breakfast were associated with obesity-related outcomes. It is worth noting that the consumption of whole fruits and nuts during the day can contribute to the prevention and management of overweight and obesity among adults [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. However, most types of whole fruits have high simple sugar content, such as sucrose, fructose, and glucose [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Whole fruit consumption at dinner was positively associated with abdominal obesity in this study, perhaps due to the overconsumption of simple sugars, which is one of the main causes of obesity and related diseases [\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Furthermore, the overconsumption of high calorie foods of nuts is often accompanied by excessive energy intake at dinner [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], which had higher odds of obesity in the current study as well. It has been reported that abdominal obesity is significantly associated with cardiovascular disease and diabetes mortality [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], which can provide a certain theoretical basis for health care. In addition, it was shown that breakfast consumers had less abdominal obesity when their breakfast was composed of fruit, natural cereal flakes, nuts, and yogurt [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Moreover, it was also observed that the ratio of macronutrients including high-quality carbohydrates and low-carbohydrates, as well as consumption of foods including whole fruits, poultry, dairy, and nuts at breakfast were related to a lower odds of having obesity. Evidence suggested that low-quality carbohydrate consumption at breakfast could secret effective insulin and incretins, which may attenuate postprandial glycemic responses and decrease insulin requirements at lunch and dinner [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Further, the low-quality carbohydrate intake at breakfast could improve glucose tolerance during the day by benefiting the metabolic and incretin systems [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. By contrast, the ratio of fat, consumption of foods including other starchy vegetables, and poultry at dinner were related to obesity-related outcomes in the present study. These results proved the converse association of macronutrients and foods with obesity-related outcomes in different meal-timing. Furthermore, replacing 1 unit of the above food consumption at dinner with the same unit of food consumption at breakfast resulted in lower odds of obesity. One possible mechanism could be that meal timing was related to markers of the circadian clock, aging, and autophagy [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Studies have shown that energy distribution throughout a day can affect body weight, blood pressure, inflammation, appetite, insulin sensitivity, and lipid profiles [\u003cspan additionalcitationids=\"CR63\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. This association was consistent with our primary analyses with further consideration of a series of traditional obesity-related dietary factors [\u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], as well as breakfast skipping, dietary quality, energy consumption status, shift or rotating workers, and sleep hours [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan additionalcitationids=\"CR69\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. After re-inclusion participants with extreme energy intake, the results were still consistent with main results. Even if this small group of participants with extreme energy intake may have no impact on the associations, these participants were still removed in order to ensure the rigor based on previous literature reported [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Further, the results were still robust when additionally consideration of timing for breakfast and dinner, which suggested that the timing for breakfast and dinner have no influence on the current study. Therefore, in dietary counselling, the emphasis should be put on not only the quality of foods, but also the meal-timing.\u003c/p\u003e \u003cp\u003e The results of this study suggest that both the quantity of macronutrients and food sources and the energy distribution throughout a day need to be taken into consideration for dietary recommendations to prevent obesity, which provides certain theoretical significance for the establishment of dietary guidelines in the field of public health and suggests that people should attach importance to the quality and meal timing of foods for the prevention of obesity.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eThis study has several strengths. First of all, it was based on the nationally representative data from the well-designed study (NHANES). Second, the association remained robust with the consideration of a series of traditional obesity-related dietary factors [\u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], as well as breakfast skipping, dinner skipping, snack consumption, dietary quality, shift workers, and sleep hours. Third, the association remained robust when the data were stratified by total energy consumption throughout the day and suggested the importance of energy distribution throughout the day. Despite the obtained results, limitations still exist in this study. First, it was a cross-sectional study and could not establish causal inferences. Second, measurement error was unavoidable for self-reported diet and other information, which may result in an overestimation or underestimation of the association. Third, a series of confounders were considered, but residual confounders might remain.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study indicated that among US adults, higher intake of energy, macronutrients (low-quality carbohydrates, fat, SFA, and USFA), and foods (whole fruits, other starchy vegetables, added sugars, poultry, dairy, and nuts) at dinner than that at breakfast was associated with higher odds of having obesity. This study also emphasized the importance of diet quality and meal-timing for the prevention of obesity.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWaist circumference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSaturated fatty acids\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUSFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnsaturated fatty acids\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHANES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAHEI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlternative Healthy Eating Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eORs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratios\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCIs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence intervals\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES protocol was approved by the Ethics Review Board of the National Center for Health Statistics Research, and all participants provided informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no other conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by funds from\u0026nbsp;the\u0026nbsp;National\u0026nbsp;Natural\u0026nbsp;Science\u0026nbsp;Foundation\u0026nbsp;of\u0026nbsp;China\u0026nbsp;(82073534 to Changhao Sun).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the participants and staff of the National Health and Nutrition Examination Survey 2003\u0026ndash;2016 for valuable contributions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor Contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll authors made a significant contribution to this article. CH.S planned the work. WY.H carried out the statistical analysis. WWQ wrote and reported the work. All authors critically assessed and reviewed the paper and approved the version to be published. CH.S are responsible for the overall content as guarantors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOgden C, Carroll M, Lawman H, Fryar C, Kruszon-Moran D, Kit B, Flegal K. Trends in Obesity Prevalence Among Children and Adolescents in the United States, 1988\u0026ndash;1994 Through 2013\u0026ndash;2014. JAMA. 2016;315(21):2292\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOgden C, Troiano R, Briefel R, Kuczmarski R, Flegal K, Johnson C. Prevalence of overweight among preschool children in the United States, 1971 through 1994. Pediatrics. 1997;99(4):E1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEgger G, Dixon J. Obesity and chronic disease: always offender or often just accomplice? Br J Nutr. 2009;102(8):1238\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEgger G, Dixon J. Non-nutrient causes of low-grade, systemic inflammation: support for a 'canary in the mineshaft' view of obesity in chronic disease. Obes Rev. 2011;12(5):339\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerg C, Lappas G, Wolk A, Strandhagen E, Tor\u0026eacute;n K, Rosengren A, Thelle D, Lissner L. Eating patterns and portion size associated with obesity in a Swedish population. Appetite. 2009;52(1):21\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKutsuma A, Nakajima K, Suwa K. Potential Association between Breakfast Skipping and Concomitant Late-Night-Dinner Eating with Metabolic Syndrome and Proteinuria in the Japanese Population. \u003cem\u003eScientifica (Cairo)\u003c/em\u003e 2014, 2014:253581.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang JB, Patterson RE, Ang A, Emond JA, Shetty N, Arab L. Timing of energy intake during the day is associated with the risk of obesity in adults. J Hum Nutr Diet. 2014;27(Suppl 2):255\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaron KG, Reid KJ, Horn LV, Zee PC. Contribution of evening macronutrient intake to total caloric intake and body mass index. Appetite. 2013;60(1):246\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsher G, Sassone-Corsi P. Time for food: the intimate interplay between nutrition, metabolism, and the circadian clock. Cell. 2015;161(1):84\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang P, Turek F. Timing of meals: when is as critical as what and how much. Am J Physiol Endocrinol metabolism. 2017;312(5):E369\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurek FW, Joshu C, Kohsaka A, Lin E, Ivanova G, McDearmon E, Laposky A, Losee-Olson S, Easton A, Jensen DR, et al. Obesity and metabolic syndrome in circadian Clock mutant mice. Science. 2005;308(5724):1043\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeshmukh-Taskar P, Nicklas T, Radcliffe J, O'Neil C, Liu Y. The relationship of breakfast skipping and type of breakfast consumed with overweight/obesity, abdominal obesity, other cardiometabolic risk factors and the metabolic syndrome in young adults. The National Health and Nutrition Examination Survey (NHANES): 1999\u0026ndash;2006. Public Health Nutr. 2013;16(11):2073\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorikawa C, Kodama S, Yachi Y, Heianza Y, Hirasawa R, Ibe Y, Saito K, Shimano H, Yamada N, Sone H. Skipping breakfast and prevalence of overweight and obesity in Asian and Pacific regions: a meta-analysis. Prev Med. 2011;53:260\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaraulet M, Vera B, Bonnet-Rubio G, G\u0026oacute;mez-Abell\u0026aacute;n P, Lee Y, Ordov\u0026aacute;s J. Lunch eating predicts weight-loss effectiveness in carriers of the common allele at PERILIPIN1: the ONTIME (Obesity, Nutrigenetics, Timing, Mediterranean) study. Am J Clin Nutr. 2016;104(4):1160\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWesnes KA, Pincock C, Richardson D, Helm G, Hails S. Breakfast reduces declines in attention and memory over the morning in schoolchildren. Appetite. 2003;41(3):329\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWesnes KA, Pincock C, Scholey A. Breakfast is associated with enhanced cognitive function in schoolchildren. An internet based study. Appetite. 2012;59(3):646\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeim N, Van Loan M, Horn W, Barbieri T, Mayclin P. Weight loss is greater with consumption of large morning meals and fat-free mass is preserved with large evening meals in women on a controlled weight reduction regimen. J Nutr. 1997;127(1):75\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTimlin MT, Pereira MA. Breakfast frequency and quality in the etiology of adult obesity and chronic diseases. Nutr Rev. 2007;65(6 Pt 1):268\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLudwig DS, Hu FB, Tappy L, Brand-Miller J. Dietary carbohydrates: role of quality and quantity in chronic disease. BMJ. 2018;361:k2340.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLudwig DS, Willett WC, Volek JS, Neuhouser ML. Dietary fat: From foe to friend? Science. 2018;362(6416):764\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReynolds A, Mann J, Cummings J, Winter N, Mete E, Te Morenga L. Carbohydrate quality and human health: a series of systematic reviews and meta-analyses. Lancet. 2019;393(10170):434\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson C, Paulose-Ram R, Ogden C, Carroll M, Kruszon-Moran D, Dohrmann S, Curtin L. National health and nutrition examination survey: analytic guidelines, 1999\u0026ndash;2010. Vital health Stat Ser 2 Data evaluation methods Res 2013(161):1\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShan Z, Rehm C, Rogers G, Ruan M, Wang D, Hu F, Mozaffarian D, Zhang F, Bhupathiraju S. Trends in Dietary Carbohydrate, Protein, and Fat Intake and Diet Quality Among US Adults, 1999\u0026ndash;2016. JAMA. 2019;322(12):1178\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShan R, Duan W, Liu L, Qi J, Gao J, Zhang Y, Du S, Han T, Pang X, Sun C et al. Low-Carbohydrate, High-Protein, High-Fat Diets Rich in Livestock, Poultry and Their Products Predict Impending Risk of Type 2 Diabetes in Chinese Individuals that Exceed Their Calculated Caloric Requirement. \u003cem\u003eNutrients\u003c/em\u003e 2018, 10(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShan Z, Rehm CD, Rogers G, Ruan M, Wang DD, Hu FB, Mozaffarian D, Zhang FF, Bhupathiraju SN. Trends in Dietary Carbohydrate, Protein, and Fat Intake and Diet Quality Among US Adults, 1999\u0026ndash;2016. JAMA. 2019;322(12):1178\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSacks F, Lichtenstein A, Wu J, Appel L, Creager M, Kris-Etherton P, Miller M, Rimm E, Rudel L, Robinson J, et al. Dietary Fats and Cardiovascular Disease: A Presidential Advisory From the American Heart Association. Circulation. 2017;136(3):e1\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKant A, Graubard B. 40-year trends in meal and snack eating behaviors of American adults. J Acad Nutr Dietetics. 2015;115(1):50\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZipf G, Chiappa M, Porter KS, Ostchega Y, Lewis BG, Dostal J. National health and nutrition examination survey: plan and operations, 1999\u0026ndash;2010. Vital Health Stat \u003cem\u003e1\u003c/em\u003e 2013(56).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoon CYL, Piponov H, Schwartz BE, Moretti VM, Schraut NB, Shah RR, Goldstein WM. Total Knee Arthroplasty in Obesity: In-Hospital Outcomes and National Trends. J Arthroplasty. 2016;31(11):2408\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFord ES, Maynard LM, Li C. Trends in mean waist circumference and abdominal obesity among US adults, 1999\u0026ndash;2012. JAMA. 2014;312(11):1151\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang D, Leung C, Li Y, Ding E, Chiuve S, Hu F, Willett W. Trends in dietary quality among adults in the United States, 1999 through 2010. JAMA Intern Med. 2014;174(10):1587\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWillett W, Howe G, Kushi L. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr. 1997;65:S1220\u0026ndash;8. discussion 1229S-1231S.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng Y, Li Y, Satija A, Pan A, Sotos-Prieto M, Rimm E, Willett W, Hu F. Association of changes in red meat consumption with total and cause specific mortality among US women and men: two prospective cohort studies. BMJ. 2019;365:l2110.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhong VW, Van Horn L, Greenland P, Carnethon MR, Ning H, Wilkins JT, Lloyd-Jones DM, Allen NB. Associations of Processed Meat, Unprocessed Red Meat, Poultry, or Fish Intake With Incident Cardiovascular Disease and All-Cause Mortality. JAMA Intern Med. 2020;180(4):503\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSt-Onge M-P, Ard J, Baskin ML, Chiuve SE, Johnson HM, Kris-Etherton P, Varady K. Meal Timing and Frequency: Implications for Cardiovascular Disease Prevention: A Scientific Statement From the American Heart Association. Circulation 2017, 135(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMesas AE, Mu\u0026ntilde;oz-Pareja M, L\u0026oacute;pez-Garc\u0026iacute;a E, Rodr\u0026iacute;guez-Artalejo F. Selected eating behaviours and excess body weight: a systematic review. Obes Rev. 2012;13(2):106\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorikawa C, Kodama S, Yachi Y, Heianza Y, Hirasawa R, Ibe Y, Saito K, Shimano H, Yamada N, Sone H. Skipping breakfast and prevalence of overweight and obesity in Asian and Pacific regions: a meta-analysis. Prev Med. 2011;53(4\u0026ndash;5):260\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzajewska H, Ruszczynski M. Systematic review demonstrating that breakfast consumption influences body weight outcomes in children and adolescents in Europe. Crit Rev Food Sci Nutr. 2010;50(2):113\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChowdhury EA, Richardson JD, Holman GD, Tsintzas K, Thompson D, Betts JA. The causal role of breakfast in energy balance and health: a randomized controlled trial in obese adults. Am J Clin Nutr. 2016;103(3):747\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeliebter A, Astbury NM, Aviram-Friedman R, Yahav E, Hashim S. Skipping breakfast leads to weight loss but also elevated cholesterol compared with consuming daily breakfasts of oat porridge or frosted cornflakes in overweight individuals: a randomised controlled trial. J Nutr Sci. 2014;3:e56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJakubowicz D, Barnea M, Wainstein J, Froy O. High caloric intake at breakfast vs. dinner differentially influences weight loss of overweight and obese women. Obes (Silver Spring). 2013;21(12):2504\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJakubowicz D, Barnea M, Wainstein J, Froy O. Effects of caloric intake timing on insulin resistance and hyperandrogenism in lean women with polycystic ovary syndrome. Clin Sci (Lond). 2013;125(9):423\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSt-Onge M, Ard J, Baskin M, Chiuve S, Johnson H, Kris-Etherton P, Varady K. Meal Timing and Frequency: Implications for Cardiovascular Disease Prevention: A Scientific Statement From the American Heart Association. Circulation. 2017;135(9):e96\u0026ndash;121.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJakubowicz D, Wainstein J, Ahr\u0026eacute;n B, Bar-Dayan Y, Landau Z, Rabinovitz H, Froy O. High-energy breakfast with low-energy dinner decreases overall daily hyperglycaemia in type 2 diabetic patients: a randomised clinical trial. Diabetologia. 2015;58(5):912\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePurslow L, Sandhu M, Forouhi N, Young E, Luben R, Welch A, Khaw K, Bingham S, Wareham N. Energy intake at breakfast and weight change: prospective study of 6,764 middle-aged men and women. Am J Epidemiol. 2008;167(2):188\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanda S. Circadian physiology of metabolism. Science. 2016;354(6315):1008\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcGinnis GR, Young ME. Circadian regulation of metabolic homeostasis: causes and consequences. Nat Sci Sleep. 2016;8:163\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar Jha P, Challet E, Kalsbeek A. Circadian rhythms in glucose and lipid metabolism in nocturnal and diurnal mammals. Mol Cell Endocrinol. 2015;418(Pt):74\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHebden L, O'Leary F, Rangan A, Singgih Lie E, Hirani V, Allman-Farinelli M. Fruit consumption and adiposity status in adults: A systematic review of current evidence. Crit Rev Food Sci Nutr. 2017;57(12):2526\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSugizaki CSA, Naves MMV. Potential Prebiotic Properties of Nuts and Edible Seeds and Their Relationship to Obesity. Nutrients 2018, 10(11).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee J. Sorbitol, Rubus fruit, and misconception. Food Chem. 2015;166:616\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Dam RM, Seidell JC. Carbohydrate intake and obesity. Eur J Clin Nutr. 2007;61(Suppl 1):S75\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBosy-Westphal A, M\u0026uuml;ller MJ. Impact of carbohydrates on weight regain. Curr Opin Clin Nutr Metab Care. 2015;18(4):389\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuyken AE, Goletzke J, Joslowski G, Felbick A, Cheng G, Herder C, Brand-Miller JC. Association between carbohydrate quality and inflammatory markers: systematic review of observational and interventional studies. Am J Clin Nutr. 2014;99(4):813\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Souza RGM, Schincaglia RM, Pimentel GD, Mota JF. Nuts and Human Health Outcomes: A Systematic Review. Nutrients 2017, 9(12).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang H, Berg E, Cheng X, Shen W. How to best assess abdominal obesity. Curr Opin Clin Nutr Metab Care. 2018;21(5):360\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChatelan A, Castetbon K, Pasquier J, Allemann C, Zuber A, Camenzind-Frey E, Zuberbuehler CA, Bochud M. Association between breakfast composition and abdominal obesity in the Swiss adult population eating breakfast regularly. Int J Behav Nutr Phys Act. 2018;15(1):115.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoore MC, Smith MS, Farmer B, Coate KC, Kraft G, Shiota M, Williams PE, Cherrington AD. Morning Hyperinsulinemia Primes the Liver for Glucose Uptake and Glycogen Storage Later in the Day. Diabetes. 2018;67(7):1237\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDimitriadis GD, Maratou E, Kountouri A, Board M, Lambadiari V. Regulation of Postabsorptive and Postprandial Glucose Metabolism by Insulin-Dependent and Insulin-Independent Mechanisms: An Integrative Approach. Nutrients 2021, 13(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonuccelli S, Muscelli E, Gastaldelli A, Barsotti E, Astiarraga BD, Holst JJ, Mari A, Ferrannini E. Improved tolerance to sequential glucose loading (Staub-Traugott effect): size and mechanisms. Am J Physiol Endocrinol Metab. 2009;297(2):E532\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJamshed H, Beyl RA, Della Manna DL, Yang ES, Ravussin E, Peterson CM. Early Time-Restricted Feeding Improves 24-Hour Glucose Levels and Affects Markers of the Circadian Clock, Aging, and Autophagy in Humans. Nutrients 2019, 11(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMattson MP, Longo VD, Harvie M. Impact of intermittent fasting on health and disease processes. Ageing Res Rev. 2017;39:46\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTinsley GM, Horne BD. Intermittent fasting and cardiovascular disease: current evidence and unresolved questions. Future Cardiol. 2018;14(1):47\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatterson RE, Sears DD. Metabolic Effects of Intermittent Fasting. Annu Rev Nutr. 2017;37:371\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan T, Gao J, Wang L, Li C, Qi L, Sun C, Li Y. The Association of Energy and Macronutrient Intake at Dinner Versus Breakfast With Disease-Specific and All-Cause Mortality Among People With Diabetes: The U.S. National Health and Nutrition Examination Survey, 2003\u0026ndash;2014. Diabetes Care. 2020;43(7):1442\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShan Z, Guo Y, Hu FB, Liu L, Qi Q. Association of Low-Carbohydrate and Low-Fat Diets With Mortality Among US Adults. JAMA Intern Med. 2020;180(4):513\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng Y, Li Y, Satija A, Pan A, Sotos-Prieto M, Rimm E, Willett WC, Hu FB. Association of changes in red meat consumption with total and cause specific mortality among US women and men: two prospective cohort studies. BMJ. 2019;365:l2110.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaslam DW, James WPT. Obesity. Lancet. 2005;366(9492):1197\u0026ndash;209.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa X, Chen Q, Pu Y, Guo M, Jiang Z, Huang W, Long Y, Xu Y. Skipping breakfast is associated with overweight and obesity: A systematic review and meta-analysis. Obes Res Clin Pract. 2020;14(1):1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonzani A, Ricotti R, Caputo M, Solito A, Archero F, Bellone S, Prodam F. A Systematic Review of the Association of Skipping Breakfast with Weight and Cardiometabolic Risk Factors in Children and Adolescents. What Should We Better Investigate in the Future? \u003cem\u003eNutrients\u003c/em\u003e 2019, 11(2).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"energy, macronutrients, food sources, energy distribution throughout a day, obesity, meal timing","lastPublishedDoi":"10.21203/rs.3.rs-4543116/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4543116/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to investigate the association of the difference of dietary consumption (energy, macronutrients, and foods) at dinner versus breakfast with obesity among U.S adults.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubjects/Methods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adopted the data from the National Health and Nutrition Examination Survey (2003–2016), involving a total of 27911 participants. The differences in the ratio of total energy and three macronutrients with six subgroups at dinner versus breakfast (ΔRatio) were categorized into quartiles. The differences in the consumption of 17 types of food at dinner versus breakfast (ΔFoods) were considered as continuous variables. Body Mass Index (BMI) and Waist circumference (WC) were used to define general obesity (30.0 ≤ BMI \u0026lt; 40.0), morbid obesity (BMI ≥ 40.0), and abdominal obesity (WC \u0026gt; 102 cm for men or WC \u0026gt; 88 cm for women). Multiple logistic and linear regression models were developed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter a variety of covariates were adjusted, participants in the highest quartile (higher energy/macronutrient intake at dinner than breakfast) of ΔRatio in terms of energy, fat, saturated fatty acids (SFA), and unsaturated fatty acids (USFA) was positively associated with morbid obesity compared with those in the lowest quartile (OR\u003csub\u003eΔRatio of energy\u003c/sub\u003e 1.27, 95%CI 1.01;1.61; OR\u003csub\u003eΔRatio of fat\u003c/sub\u003e 1.27, 95%CI 1.01;1.60; OR\u003csub\u003eΔRatio of SFA\u003c/sub\u003e 1.27, 95%CI 1.01;1.59; OR\u003csub\u003eΔRatio of USFA\u003c/sub\u003e 1.28, 95%CI 1.02;1.59). The highest quartile of ΔRatio of low-quality carbohydrate led to higher odds of abdominal obesity (OR\u003csub\u003eΔRatio of low−quality carbohydrate\u003c/sub\u003e 1.16; 95%CI 1.03–1.31). Meanwhile, ΔRatio of low-quality carbohydrates was significantly positively associated with BMI (coefficient: 0.562, 95%CI: 0.217–0.907). ΔFoods including whole fruits, other starchy vegetables, added sugars, poultry, dairy, and nuts were positively associated with obesity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study indicated that among US adults, higher intake of energy, macronutrients (low-quality carbohydrate, fat, SFA, and USFA), and foods (whole fruits, other starchy vegetables, added sugars, poultry, dairy, and nuts) at dinner than breakfast was associated with higher odds of having obesity. In conclusion, this study emphasized the importance of diet quality and meal-timing in the prevention of obesity.\u003c/p\u003e","manuscriptTitle":"The association of energy, macronutrients, and food sources consumption at dinner versus breakfast with obesity: The National Health and Nutrition Examination Survey (NHANES), 2003-2016","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-27 19:10:52","doi":"10.21203/rs.3.rs-4543116/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dfa82170-b3de-4c98-9e72-96e05aee3b29","owner":[],"postedDate":"June 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-05T04:05:57+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-27 19:10:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4543116","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4543116","identity":"rs-4543116","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}