Meal Timing of Subtypes of Macronutrients Consumption With Cardiovascular Diseases

NHANES, 2003 to 2016

Wanying Hou; Jian Gao; Wenbo Jiang; Wei Wei; Huanyu Wu; Yuntao Zhang; Changhao Sun; Ying Li; Tianshu Han

J Clin Endocrinol Metab. 2021;106(7):e2480-e2490.

Abstract and Introduction

Abstract

Context: Emerging evidence suggests that not only the quantity but also the quality and food sources of macronutrients plays an important role in CVD. However, limited studies have examined the association of meal timing of different quality of macronutrients with CVD risk.

Objective: This study aimed to examine the association of subtypes of macronutrient consumption at dinner vs breakfast with cardiovascular diseases (CVD).

Methods: A total of 27 911 participants from the National Health and Nutrition Examination Survey (2003–2016) were included. The differences of subtypes of macronutrients at dinner vs breakfast (Δratio) were categorized into quintiles. Multiple logistic regression models and isocaloric substitution effects of subtypes were performed.

Results: After adjustment of a variety of covariates, participants in the highest quintile of the Δratio of low-quality carbohydrates had a higher risk of angina (odds ratio [OR] = 1.63; 95% CI, 1.16–2.29) (P _{for trend} = .007) and heart attack (OR = 1.47; 95% CI, 1.13–1.93) (P _{for trend} = .068) compared with the lowest quintile. The highest quintile of the Δratio of animal protein had a higher risk of coronary heart disease (OR = 1.44; 95% CI, 1.06–1.95) (P _{for trend} = .014) and angina (OR = 1.44; 95% CI, 1.01–2.07) (P _{for trend} = .047). For the Δratio of unsaturated fatty acid (USFA), the highest quintile of the Δratio of USFA was related to lower stroke risk (OR = 0.76; 95% CI, 0.58–0.99) (P _{for trend} = .049). Isocaloric substitution of low-quality carbohydrates/animal protein by high-quality carbohydrates/plant protein at dinner reduced CVD risk by around 10%.

Conclusion: This study indicated that overconsumption of low-quality carbohydrates and animal protein at dinner rather than breakfast was significantly associated with higher CVD risk and USFA consumption at dinner related to lower CVD risk among US adults. Substitution of low-quality carbohydrates or animal protein by high-quality carbohydrates or plant protein at dinner could reduce CVD risk.

Introduction

Cardiovascular disease (CVD) is the main cause of mortality regardless of race, ethnicity, or sex, and almost 46% of noncommunicable disease deaths are attributable to CVD around the globe.^[1,2] Diet plays a critical role in the prevention and treatment of CVD.^[3] Emerging evidence suggests that not only the quantity but also the quality and food sources of macronutrients is a relevant aspect of nutrition and plays an important role in human health.^[4–7] However, limited studies have examined the association of meal timing of different quality of macronutrients with CVD risk.

Nowadays it appears that meal timing has major effects on metabolic and physiological parameters.^[8] For example, breakfast skipping,^[9,10] late lunch eating,^[11] and high energy intake at dinner^[12–15] were related to a higher risk of obesity, as well as lower overall diet quality and poorer cognitive performance. Late-night eating has been related to a higher risk of poor cardiometabolic health in several observational studies.^[12,14,16] Further, it has been reported that dietary patterns with higher energy load in the evening may lead to metabolic syndrome by the deterioration of postprandial glucose and insulin.^[17] On the other hand, a high-energy breakfast with a reduced dinner was suggested to be beneficial and considered to be a useful alternative for the management of obesity and metabolic syndrome.^[18] Meanwhile, a large number of studies have indicated that time-restricted feeding is beneficial for a variety of metabolic responses, reducing insulin resistance, and increasing glucose tolerance.^[19–22] During the past 2 decades, the overall macronutrient consumption has remained unchanged among US adults, but subtypes of macronutrients obviously changed.^[23] However, to the best of our knowledge, no studies have examined the association of macronutrients with CVD risk by considering subtypes and meal timing of macronutrients.

In the present study, we classified subtypes of macronutrients based on the food sources using data from the US National Health and Nutrition Examination Survey (NHANES) and examined the association of subtypes consumption at dinner vs breakfast with CVD risk.

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Table 1. Characteristics of participants across the US National Health and Nutrition Examination Survey cycle, 2003 to 2016
Characteristics	2003–2004 (N = 3572)	2005–2006 (N = 3493)	2007–2008 (N = 4242)	2009–2010 (N = 4596)	2011–2012 (N = 3983)	2013–2014 (N = 4140)	2015–2016 (N = 3885)	P
Age, y	47.01 (45.99–48.04)	47.46 (45.84–49.09)	47.71 (46.78–48.65)	47.83 (46.76–48.91)	48.06 (46.39–49.74)	47.87 (47.03–48.70)	48.99 (47.72–50.26)	.451
Female, %	52.30 (50.48–54.11)	52.85 (51.36–54.33)	53.02 (51.40–54.64)	53.03 (51.88–54.17)	52.60 (50.97–54.23)	52.91 (51.00–54.82)	52.36 (50.86–53.86)	.988
Non-Hispanic White, %	73.99 (67.06–79.90)	74.27 (68.45–79.33)	72.08 (65.12–78.12)	70.41 (63.38–76.58)	68.93 (61.27–75.67)	67.57 (60.77–73.70)	67.68 (59.78–74.69)	.890
BMI	28.25 (27.88–28.62)	28.67 (28.16–29.17)	28.62 (28.26–28.98)	28.73 (28.47–28.99)	28.84 (28.41–29.27)	29.11 (28.73–29.48)	29.47 (28.91–30.04)	.013
College graduate or above, %	24.47 (21.07–28.23)	27.46 (23.22–32.14)	27.01 (22.83–31.64)	29.90 (27.34–32.60)	32.49 (27.70–37.69)	32.23 (28.40–36.33)	34.27 (28.07–41.05)	.003
> $100 000 annual household income, %	–	–	18.90 (15.33–23.07)	21.83 (19.70–24.13)	24.17 (18.86–30.42)	26.68 (22.32–31.55)	26.86 (21.58–32.90)	< .001
Exercise regularly, %	17.26 (15.20–19.25)	16.21 (14.59–17.97)	21.41 (18.95–24.09)	20.54 (18.35–22.56)	24.03 (21.93–26.26)	33.61 (31.01–36.32)	21.14 (18.30–24.30)	< .001
Current smoker, %	28.13 (25.50–30.91)	26.17 (23.66–28.85)	25.26 (22.63–28.08)	21.95 (20.25–23.75)	22.15 (19.98–24.49)	19.74 (17.41–22.29)	19.84 (17.34–22.60)	< .001
Current drinker, %	68.67 (64.68–72.40)	71.84 (67.49–75.81)	71.60 (68.61–74.41)	73.26 (70.66–75.70)	76.32 (74.11–78.40)	75.69 (70.81–79.98)	73.67 (70.50–76.62)	.001
Medicine use for lower blood sugar, %	5.23 (4.14–6.59)	5.89 (4.91–7.04)	7.07 (5.73–8.70)	6.79 (6.08–7.57)	7.35 (6.34–8.51)	7.37 (6.56–8.27)	9.01 (7.79–10.40)	< .001
Medicine use for hypertension, %	20.93 (18.57–23.50)	22.03 (19.82–24.41)	23.92 (22.00–25.95)	24.29 (21.41–27.43)	24.14 (21.51–26.98)	24.97 (23.14–26.89)	24.80 (22.64–27.10)	.013
Medicine use for cholesterol, %	17.15 (15.03–19.50)	17.95 (15.52–20.67)	21.28 (19.70–22.95)	19.96 (18.32–21.70)	23.45 (21.29–25.76)	25.36 (23.40–27.42)	26.16 (23.98–28.46)	< .001
Total energy, kcal/d	2100.94 (2067.50–2134.39)	2081.53 (2042.14–2120.92)	2040.53 (1996.73–2084.33)	2043.95 (2010.41–2077.49)	2071.38 (2030.13–2112.63)	2034.06 (2007.56–2060.56)	2043.81 (2007.32–2080.30)	.057
High-quality carbohydrate, serving/d	2.69 (2.50–2.88)	2.60 (2.45–2.76)	2.64 (2.44–2.83)	2.90 (2.80–3.00)	3.02 (2.86–3.19)	2.88 (2.77–2.98)	2.87 (2.69–3.05)	.003
Low-quality carbohydrate, serving/d	23.19 (22.21–24.16)	22.69 (21.96–23.41)	22.82 (21.66–23.98)	22.07 (21.59–22.55)	21.71 (20.99–22.43)	21.43 (20.73–22.12)	20.75 (19.95–21.55)	.002
SFA, g/d	26.29 (25.53–27.05)	26.45 (25.73–27.18)	25.52 (24.66–26.38)	24.86 (24.11–25.61)	24.93 (24.17–25.69)	25.33 (24.81–25.84)	26.64 (25.94–27.34)	.001
USFA, g/d	47.07 (45.79–48.35)	46.17 (44.83–47.52)	45.36 (44.14–46.59)	44.48 (43.57–45.40)	46.51 (45.39–47.63)	45.81 (44.87–46.74)	47.26 (46.10–48.42)	.006
Animal protein, serving/d	4.51 (4.37–4.65)	4.67 (4.59–4.76)	4.68 (4.62–4.74)	4.77 (4.68–4.87)	4.55 (4.49–4.61)	4.78 (4.70–4.85)	3.29 (3.21–3.37)	< .001
Plant protein, serving/d	7.02 (6.84–7.19)	6.90 (6.79–7.01)	6.89 (6.67–7.11)	7.09 (6.99–7.20)	7.22 (7.05–7.39)	7.14 (7.03–7.26)	7.12 (6.96–7.28)	.016
AHEI	55.04 (54.30–55.79)	53.86 (53.11–54.62)	53.34 (52.45–54.22)	53.95 (53.37–54.52)	54.03 (53.22–54.85)	53.15 (52.57–53.73)	59.34 (58.51–60.17)	< .001
Dietary supplement use, %	55.04 (51.81–58.22)	55.79 (53.46–58.10)	52.50 (49.09–55.90)	51.64 (49.35–53.91)	53.63 (51.02–56.23)	55.77 (52.37–59.11)	57.83 (53.84–61.72)	.256
Congestive heart failure, %	2.40 (1.84–3.13)	2.23 (1.83–2.73)	2.31 (1.80–2.95)	1.83 (1.39–2.40)	2.40 (1.77–3.25)	2.25 (1.88–2.67)	2.02 (1.68–2.42)	.681
Coronary heart disease, %	4.12 (3.29–5.13)	3.33 (2.67–4.14)	3.45 (2.91–4.09)	3.39 (2.75–4.17)	2.96 (2.16–4.05)	3.73 (2.92–4.76)	3.61 (2.82–4.62)	.600
Angina, %	3.20 (2.30–4.43)	2.45 (1.98–3.03)	2.08 (1.56–2.77)	2.03 (1.53–2.71)	2.06 (1.58–2.68)	2.10 (1.64–2.67)	1.94 (1.51–2.48)	.074
Heart attack, %	4.07 (3.37–4.92)	3.31 (2.48–4.42)	3.42 (2.87–4.05)	3.08 (2.43–3.91)	3.38 (2.83–4.02)	3.02 (2.57–3.54)	3.27 (2.67–4.00)	.460
Stroke, %	2.50 (1.94–3.20)	2.54 (1.93–3.35)	3.26 (2.39–4.43)	2.48 (2.07–2.97)	2.31 (1.84–2.89)	2.75 (2.34–3.24)	2.48 (1.94–3.18)	.464

All data analyses in the present study were based on weighted estimates with sample weights provided by the US National Health and Nutrition Examination Survey (NHANES). Continuous variables were presented as weighted means (95% CIs). Categorical variables were presented as weighted percentages (95% CIs). P values were calculated by a general linear model for continuous variables adjusting for age and the chi-square test for categorical variables.
Abbreviations: AHEI, Alternative Healthy Eating Index; BMI, body mass index; SFA, saturated fatty acid; USFA, unsaturated fatty acid.

Table 2. Differences of total macronutrients consumption during the day across cardiovascular disease patients
Total macronutrient consumption	NHANES, 2003 to 2016
Total macronutrient consumption	Congestive heart failure (N = 848)	Non–congestive heart failure (N = 27 063)	P	Coronary heart disease (N = 1182)	Non–coronary heart disease (N = 26 729)	P	Angina (N = 778)	Non-angina (N = 27 133)	P	Heart attack (N = 1203)	Non–heart attack (N = 26 708)	P	Stroke (N = 971)	Non-stroke (N = 26 940)	P
High-quality carbohydrates, serving/d	2.41 (2.24–2.57)	2.81 (2.75–2.88)	< .001	2.65 (2.49–2.81)	2.81 (2.75–2.87)	.037	2.56 (2.37–2.74)	2.81 (2.75–2.87)	.005	2.42 (2.27–2.57)	2.82 (2.75–2.88)	< .001	2.32 (2.17–2.47)	2.82 (2.75–2.88)	< .001
Low-quality carbohydrates, serving/d	23.68 (22.77–24.59)	22.03 (21.71–22.35)	< .001	22.76 (21.92–23.61)	22.04 (21.72–22.36)	.068	23.62 (22.64–24.61)	22.03 (21.71–22.35)	.001	23.58 (22.60–24.56)	22.01 (21.69–22.33)	.001	24.80 (23.88–25.73)	21.99 21.67–22.31)	< .001
SFA, g/d	24.26 (23.24–25.27)	25.74 (25.46–26.02)	.004	25.20 (24.33–26.08)	25.73 (25.44–26.02)	< .001	24.14 (22.80–25.48)	25.75 (25.47–26.02)	.014	24.71 (23.74–25.68)	25.75 (25.47–26.03)	.033	24.33 (23.34–25.31)	25.75 (25.47–26.03)	.004
USFA, g/d	43.05 (41.14–44.96)	46.17 (45.73–46.61)	.001	45.70 (44.19–47.22)	46.12 (45.66–46.57)	.606	43.52 (41.15–45.89)	46.16 (45.71–46.61)	.033	44.08 (42.58–45.57)	46.17 (45.73–46.62)	.007	41.35 (39.83–42.86)	46.23 (45.79–46.66)	< .001
Animal protein, serving/d	4.54 (4.40–4.68)	4.46 (4.42–4.49)	.255	4.39 (4.25–4.53)	4.46 (4.42–4.50)	.339	4.35 (4.20–4.50)	4.46 (4.42–4.50)	.159	4.53 (4.39–4.67)	4.46 (4.42–4.49)	.309	4.46 (4.30–4.62)	4.46 (4.42–4.50)	.970
Plant protein, serving/d	6.84 (6.64–7.05)	7.06 (7.00–7.12)	.037	7.29 (7.07–7.51)	7.05 (6.99–7.11)	.034	7.18 (6.96–7.40)	7.06 (7.00–7.12)	.275	6.99 (6.80–7.19)	7.06 (7.00–7.12)	.491	6.84 (6.64–7.04)	7.06 (7.00–7.12)	.024

All data analyses in the present study were based on weighted estimates with sample weights provided by the NHANES. Continuous variables were presented as weighted means (95% CIs). P values were calculated by general linear models adjusting for age.
Abbreviations: AHEI, Alternative Healthy Eating Index; BMI, body mass index; NHANES, US National Health and Nutrition Examination Survey; SFA, saturated fatty acid; USFA, unsaturated fatty acid.

Table 3. Association of Δ ratio of subtypes of macronutrients with cardiovascular diseases
Δ Ratio	Congestive heart failure	Coronary heart disease	Angina	Heart attack	Stroke
High-quality carbohydrates
Q1	1.00 (reference)	1.00 (reference)	1.00 (reference)	1.00 (reference)	1.00 (reference)
Q2	0.95 (0.67–1.34)	1.04 (0.79–1.37)	0.88 (0.63–1.22)	0.93 (0.72–1.21)	0.81 (0.60–1.11)
Q3	0.75 (0.54–1.04)	1.03 (0.80–1.32)	0.90 (0.67–1.20)	0.98 (0.73–1.32)	0.68 (0.51–0.90)
Q4	0.78 (0.56–1.08)	1.02 (0.74–1.39)	0.95 (0.70–1.29)	0.95 (0.74–1.22)	0.90 (0.67–1.21)
P _{for trend}	.078	.921	.848	.825	.483
Low-quality carbohydrates
Q1	1.00 (reference)	1.00 (reference)	1.00 (reference)	1.00 (reference)	1.00 (reference)
Q2	1.12 (0.84–1.50)	1.19 (0.87–1.63)	1.74 (1.26–2.41)	1.75 (1.37–2.24)	1.00 (0.77–1.31)
Q3	1.04 (0.77–1.42)	1.39 (1.06–1.83)	1.85 (1.31–2.62)	1.30 (1.01–1.68)	0.99 (0.76–1.29)
Q4	1.02 (0.75–1.39)	1.21 (0.91–1.61)	1.63 (1.16–2.29)	1.47 (1.13–1.93)	1.13 (0.86–1.49)
P _{for trend}	.992	.144	.007	.068	.396
SFA
Q1	1.00 (reference)	1.00 (reference)	1.00 (reference)	1.00 (reference)	1.00 (reference)
Q2	0.89 (0.62–1.26)	1.13 (0.87–1.48)	1.08 (0.77–1.51)	0.91 (0.71–1.16)	0.77 (0.60–1.00)
Q3	0.88 (0.61–1.27)	1.05 (0.78–1.42)	1.21 (0.82–1.80)	0.87 (0.65–1.16)	0.79 (0.60–1.03)
Q4	0.80 (0.56–1.14)	1.11 (0.83–1.49)	1.13 (0.83–1.55)	1.07 (0.82–1.40)	0.76( 0.57–1.01)
P _{for trend}	.239	.593	.378	.544	.108
USFA
Q1	1.00 (reference)	1.00 (reference)	1.00 (reference)	1.00 (reference)	1.00 (reference)
Q2	1.04 (0.72–1.50)	0.94 (0.73–1.21)	1.16 (0.83–1.63)	1.07 (0.83–1.39)	0.88 (0.69–1.13)
Q3	1.04 (0.79–1.36)	1.14 (0.84–1.56)	1.06 (0.76–1.47)	1.09 (0.85–1.40)	0.86 (0.65–1.13)
Q4	0.87 (0.63–1.21)	1.16 (0.86–1.54)	1.14 (0.85–1.53)	1.07 (0.82–1.39)	0.76 (0.58–0.99)
P _{for trend}	.389	.191	.508	.6	.049
Animal protein
Q1	1.00 (reference)	1.00 (reference)	1.00 (reference)	1.00 (reference)	1.00 (reference)
Q2	0.85 (0.64–1.14)	1.24 (0.95–1.62)	1.22 (0.89–1.68)	0.98 (0.77–1.23)	1.04 (0.79–1.36)
Q3	1.08 (0.81–1.43)	1.44 (1.12–1.87)	1.31 (0.93–1.84)	1.03 (0.78–1.34)	0.96 (0.76–1.21)
Q4	1.03 (0.77–1.38)	1.44 (1.06–1.95)	1.44 (1.01–2.07)	1.13 (0.88–1.46)	0.93 (0.68–1.26)
P _{for trend}	.496	.014	.047	.266	.511
Plant protein
Q1	1.00 (reference)	1.00 (reference)	1.00 (reference)	1.00 (reference)	1.00 (reference)
Q2	1.30 (0.99–1.71)	1.22 (0.93–1.60)	1.11 (0.83–1.47)	1.12 (0.89–1.40)	1.50 (1.18–1.91)
Q3	0.94 (0.68–1.29)	1.18 (0.92–1.50)	0.98 (0.74–1.31)	1.03 (0.80–1.34)	1.23 (0.96–1.58)
Q4	0.74 (0.52–1.07)	1.34 (0.97–1.84)	1.20 (0.90–1.60)	1.19 (0.91–1.56)	1.05 (0.82–1.34)
P _{for trend}	.056	.084	.323	.276	.894

Adjustments included age, sex, ethnicity, income, education, exercise, smoke, alcohol intake, supplement use, BMI, medication use for lower blood sugar, medication use for hypertension, medication use for cholesterol, and total intake of energy, high-quality carbohydrates, SFA, animal protein, AHEI, and breakfast skipping.
Abbreviations: AHEI, Alternative Healthy Eating Index; BMI, body mass index; SFA, saturated fatty acid; Q, quintile

Table 4. Isocaloric substitution effects with one-serving decrease of low-quality carbohydrate or animal protein food and simultaneous one-serving increase of high-quality carbohydrate or plant protein food at dinner with cardiovascular diseases
Isocaloric substitution effect	Congestive heart failure	Coronary heart disease	Angina	Heart attack	Stroke
Substitution with low-quality carbohydrate by high-quality carbohydrate	0.90 (0.82–0.98)	0.95 (0.89–1.01)	0.93 (0.86–1.01)	0.95 (0.89–1.01)	0.88 (0.81–0.96)
Substitution with animal protein by plant protein	0.90 (0.83–0.98)	1.06 (0.99–1.15)	0.99 (0.92–1.06)	0.92 (0.86–0.99)	1.03 (0.95–1.11)

Adjustments included age, sex, ethnicity, income, education, exercise, smoke, alcohol intake, supplement use, BMI, medication use for lower blood sugar, medication use for hypertension, medication use for cholesterol, and total intake of energy, high-quality carbohydrate, SFA, animal protein, AHEI, and breakfast skipping.
Abbreviations: AHEI, Alternative Healthy Eating Index; BMI, body mass index; SFA, saturated fatty acid.

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Meal Timing of Subtypes of Macronutrients Consumption With Cardiovascular Diseases

NHANES, 2003 to 2016

Abstract and Introduction

Abstract

Introduction

Tables

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Tables

Tables

References

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Authors and Disclosures

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