Prospective Study of Breakfast Eating and Incident Coronary Heart Disease in a Cohort of Male US Health Professionals
Open accessAbstract
Background—: Among adults, skipping meals is associated with excess body weight, hypertension, insulin resistance, and elevated fasting lipid concentrations. However, it remains unknown whether specific eating habits regardless of dietary composition influence coronary heart disease (CHD) risk. The objective of this study was to prospectively examine eating habits and risk of CHD. Methods and Results—: Eating habits, including breakfast eating, were assessed in 1992 in 26 902 American men 45 to 82 years of age from the Health Professionals Follow-up Study who were free of cardiovascular disease and cancer. During 16 years of follow-up, 1527 incident CHD cases were diagnosed. Cox proportional hazards models were used to estimate relative risks and 95% confidence intervals for CHD, adjusted for demographic, diet, lifestyle, and other CHD risk factors. Men who skipped breakfast had a 27% higher risk of CHD compared with men who did not (relative risk, 1.27; 95% confidence interval, 1.06–1.53). Compared with men who did not eat late at night, those who ate late at night had a 55% higher CHD risk (relative risk, 1.55; 95% confidence interval, 1.05–2.29). These associations were mediated by body mass index, hypertension, hypercholesterolemia, and diabetes mellitus. No association was observed between eating frequency (times per day) and risk of CHD. Conclusions—: Eating breakfast was associated with significantly lower CHD risk in this cohort of male health professionals.
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The most common measure used to assess overall diet quality was a previously validated and reliability tested a priori diet quality index reflecting adherence to dietary guidelines for the country of the sample population.
“The most common measure used to assess overall diet quality was a previously validated and reliability tested a priori diet quality index which reflects an individual's adherence to the dietary guidelines for the country of the sample population (for example, the Healthy Eating Index (HEI), the Alternative HEI (AHEI) and the Dietary Guidelines Index (DGI))( 23 , 28 , 86 , 88 – 90 , 92 , 94 ).”
From Understanding meal patterns: definitions, methodology and impact on nutrient intake and diet quality (2015) · cites this paper as [28]
The lack of association in two of the latter studies may be explained by their respective study populations: overall diet quality was high among men in the US Health Professionals Follow-up study, and Dewolfe & Millan used a small convenience sample of older adults from a single region in Canada.
“However, the lack of association in two of these latter studies may be explained by their respective study populations; overall diet quality was high among men in the US Health Professionals Follow-up study( 28 ) while Dewolfe & Millan( 91 ) used a small convenience sample of eighty-four female and twenty-one male older adults from a single region in Canada.”
From Understanding meal patterns: definitions, methodology and impact on nutrient intake and diet quality (2015) · cites this paper as [28]
Preliminary evidence suggests that the timing of energy and/or macronutrient intake during the day is associated with cardiometabolic risk.
“This may be an important area of research in light of preliminary evidence suggesting that the timing of energy and/or macronutrient intake during the day is associated with cardiometabolic risk( 27 , 28 , 99 , 100 ).”
From Understanding meal patterns: definitions, methodology and impact on nutrient intake and diet quality (2015) · cites this paper as [28]
In the US Health Professionals Follow-up study, Cahill et al. found no association between late night eating (defined as eating after going to bed) and AHEI scores.
“In the US Health Professionals Follow-up study, Cahill et al. ( 28 ) found no association between late night eating (defined as eating after going to bed) and AHEI scores; however, as mentioned previously, the authors acknowledged that AHEI scores were high in this sample, irrespective of their reported meal patterns.”
From Understanding meal patterns: definitions, methodology and impact on nutrient intake and diet quality (2015) · cites this paper as [28]
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- Authors
- Cahill, L. E., Chiuve, S. E., Mekary, R. A., Jensen, M. K., Flint, A. J., Hu, F. B., & Rimm, E. B.
- Affiliations
- Brigham and Women's Hospital (US)
- Venue
- Circulation
- Published
- 2013-07-23
- Volume
- 128
- Issue
- 4
- Pages
- 337-343
- ISSN
- 0009-7322 (ISSN-L)
- 1524-4539
- PMC
- PMC3797523
- OpenAlex
- W1987657839
- Cited by
- 317
- References
- 51
- Citations by year
- 2026: 17 · 2025: 19 · 2024: 32 · 2023: 24 · 2022: 35 · 2021: 33 · 2020: 25 · 2019: 20 · 2018: 23 · 2017: 27 · 2016: 20 · 2015: 19 · 2014: 19 · 2013: 4
- FWCI
- 14.35
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Circulation (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- Funders
- Canadian Institutes of Health Research
- National Heart, Lung, and Blood Institute (R01HL060712, R01HL035464, R01 HL35464, R01HL60712)
- National Cancer Institute (P01CA055075)
- National Institute of Diabetes and Digestive and Kidney Diseases (T32DK007703)
- Topics
- Health Sciences › Medicine › Physiology › Dietary Effects on Health
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Nutritional Studies and Diet
- Health Sciences › Nursing › Nutrition and Dietetics › Nutrition, Health and Food Behavior
- Keywords
- breakfast skipping, coronary artery disease, dietary habits, late-night eating, meal frequency, body mass index, hypertension, hypercholesterolemia, diabetes mellitus, cardiovascular risk factors, Health Professionals Follow-up Study
- MeSH terms
- Aged, Aged, 80 and over, Coronary Disease, Feeding Behavior, Follow-Up Studies, Health Personnel, Humans, Life Style, Male, Middle Aged, Prospective Studies, Risk Factors, United States, Incidence, Multivariate Analysis, Proportional Hazards Models, Age Distribution, Breakfast
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