Breakfast Skippers Beware: Newly Published Data on Breakfast Patterns Identifies Association with Atherosclerosis, Independent of Cardiovascular Disease Risk Factors

Breakfast Skippers Beware: Newly Published Data on Breakfast Patterns Identifies Association with Atherosclerosis, Independent of Cardiovascular Disease Risk Factors

Insights from the Progression of Early Subclinical Atherosclerosis (PESA) Study

By Kristen N Smith, PhD, RD, LD; Mary R Dicklin, PhD; Kevin C Maki, PhD

 Background: It is well accepted that a person’s lifestyle may impact markers of overall health. Factors associated with lifestyle may be dependent on cultural, social and psychological practices as they fit into a daily routine. Researchers have paid particular attention to the lifestyle habit of breakfast consumption (or non-consumption) and how it may contribute to disease risk.

Whether or not a person consumes breakfast has correlations to such factors as:

  • Measures of satiety,
  • Daily energy intake,
  • Metabolic efficiency of the diet,
  • Appetite regulation.

The regular omission of breakfast has associations with increased cardiovascular health markers such as obesity, diabetes, and unfavorable lipid profiles. Although there have been studies investigating the impacts of breakfast skipping behaviors with heart disease risk, the current study may be the first to research an association between breakfast patterns and subclinical atherosclerosis. The aim of the Progression of Early Subclinical Atherosclerosis (PESA) study1 was to characterize the association between different breakfast patterns and cardiovascular disease (CVD) risk factors; especially focusing on whether the regular omission of breakfast is associated with subclinical atherosclerosis (noted by investigating the presence of atherosclerotic plaques in the carotid arteries, aorta, and iliofemoral arteries or coronary artery calcium in a population with no previous CVD history).

 Methods: The PESA study is an ongoing observational prospective cohort of 4,082 employees of the Bank Santander Headquarters in Madrid, Spain. Male and female volunteers, aged 40 to 54 years old, were included if they met the following criteria:

  • Free of any CVD or chronic kidney disease,
  • No previous transplant,
  • Did not exceed body mass index (BMI) of 40 kg/m2,
  • Did not have any disease that might affect life expectancy and decrease it to <6 years.

Estimates of usual diet were determined through the use of a computerized questionnaire, which was developed and validated in the Estudio de Nutrición y Riesgo Cardiovascular (ENRICA) study of a Spanish population and contains nutritional information on 861 food items including many typically consumed Spanish meals and dishes. Subjects reported the foods consumed in the past 15 days while also noting specific occasions throughout the day (waking up, breakfast, mid-morning, lunch, mid-afternoon, and dinner). To characterize breakfast patterns, the researchers first utilized the quantitative description of breakfast provided by Timlin and Pereira2: “the first meal of the day that breaks the fast after the longest period of sleep, eaten before the start of daily activities (e.g., errands, travel, work), within 2 h of waking, typically no later than 10:00 in the morning, and an energy level between 20 and 35% of total daily energy need.” Additional input was gathered by application of a qualitative definition of breakfast by O’Neill et al. where breakfast is defined as “a food or beverage from at least one food group, and may be consumed at any location. Coffee, water and nonalcoholic beverages are not included in a food group.”3 Mean energy intake of the subjects in the PESA study was 2,314 kcal/day; three major breakfast groups were identified:

  • <5% total energy intake (EI) = skipping breakfast (SBF)
  • 5 to 20% total EI from breakfast = low-energy breakfast (LBF)
  • >20% total EI from breakfast = high-energy breakfast (HBF)

Anthropometric data were collected and CVD risk factors and metabolic syndrome (MetS) were assessed. The European Society of Cardiology CVD risk assessment tool, the Systematic Coronary Risk Evaluation, was used to assess fatal cardiovascular risk. Additionally, researchers noted variables such as age, gender, marital status, highest educational level, smoking status, diet practices and physical activity. Specific ultrasound equipment was utilized to assess atherosclerotic plaque in multiple vascular areas: bilateral carotid, infrarenal abdominal aorta and iliofemoral arteries. Plaques were defined as “focal protrusion into the arterial lumen of thickness >0.5 mm or >50% of the surrounding intima-media thickness or a diffuse thickness >1.5 mm measured between the media-adventitia and intima-lumen interfaces.” Coronary artery calcium (CAC) was also assessed, and states of atherosclerosis were defined as follows:


State of Atherosclerosis Definition
Subclinical atherosclerosis The presence of plaque in the right carotid, left carotid, aorta, right iliofemoral, or left iliofemoral or as the presence of calcium (CAC score > 0) in the coronary arteries
Non-coronary atherosclerosis The presence of plaque in the above areas and excluding CAC
Generalized atherosclerosis Dependent on the number of sites affected with atherosclerosis; 4 to 6 sites affected


Results: Of the 4,052 participants, 2.9%, 69.4% and 27.7% fell into the SBF (breakfast skipping), LBF (low-energy breakfast) and HBF (high-energy breakfast) categories, respectively. Compared with HBF and LBF, the SBF group was made up mostly of men, current smokers, subjects who had reportedly changed their diet in the previous year to lose weight, and subjects who consumed their highest energy intake at lunch. Compared with HBF, the LBF subjects were more likely to be men with lower education level and to be current smokers with higher calorie intakes at lunch.

Regarding diet quality, the subjects in the SBF group were most likely to have higher energy, protein (from animal sources) and dietary cholesterol intakes while also having the lowest fiber and carbohydrate intakes and greatest consumption of alcoholic and sugar-sweetened beverages and red meat.  The LBF group (compared with HBF) had higher overall energy, animal protein and cholesterol intakes and lower intakes of sugar and polysaccharides while also having dietary patterns lower in fruits, vegetables, whole grains and olive oil and higher in refined grains, red meat, fast food and precooked meals (as well as lean meat and seafood). The HBF group had the greatest intakes of dietary fiber, fruits and vegetables, whole grains and high-fat dairy.

The cardiometabolic risk marker profile was less favorable in the LBF and SBF groups, including higher levels of waist circumference, BMI, blood pressure, blood lipids and fasting blood glucose. Participants in the SBF group had a greater likelihood of scoring high on the European Society of Cardiology Systematic Coronary Risk Evaluation risk scale. Probabilities of obesity, abdominal obesity, MetS, low high-density lipoprotein cholesterol, and hypertension were significantly greater for the SBF group compared with HBF.  The prevalence values for atherosclerosis (subclinical, non-coronary and generalized) across all PESA subjects were 62.5%, 60.3% and 13.4%, respectively.

The odds ratios (ORs) for subclinical atherosclerosis were significantly elevated in the SBF group compared with the HBF group:

  • Abdominal aorta - OR 1.79, 95% confidence interval (CI) 1.16 to 2.77,
  • Carotid atherosclerotic plaques - OR 1.76, 95% CI 1.17 to 2.65,
  • Iliofemoral plaques - OR 1.72, 95% CI 1.11 to 2.64,
  • Coronary atherosclerosis – OR 1.55, 95% CI 0.97 to 2.46,
  • Non-coronary and generalized atherosclerosis - OR 2.57, 95% CI 1.54 to 4.31.

The participants in the LBF group had greater risk of carotid or iliofemoral atherosclerotic plaques compared with the HBF group (OR 1.21; 95% CI 1.03 to 1.43 and OR 1.17; 95% CI 1.00 to 1.37, respectively).

Comment: The results from the PESA study indicate that regular skipping of breakfast was associated with 1.55- to 2.57-fold higher odds for subclinical atherosclerosis, even after adjustment for traditional CVD risk factors and diet quality. Breakfast skipping behavior was also linked to an overall unhealthy lifestyle (poor overall diet, higher consumption of alcohol and smoking).  Other researchers have also noted these same associations in that skipping breakfast is often associated with smoking4, greater total energy intake5, and noncompliance with “Healthy Eating” recommendations.6

Results from PESA confirm the association between breakfast skipping and an adverse cardiometabolic risk marker profile and further show that breakfast skipping is independently associated with subclinical measures of atherosclerosis.  However, the degree to which this association might be causal vs. reflective of residual confounding due to greater exposure to CVD risk markers over time is uncertain.

Several studies have demonstrated that insulin sensitivity shows diurnal variation.  For example, Saad et al. reported that mean values for an index of insulin sensitivity produced from postprandial responses to identical meals at breakfast, lunch and dinner were 11.2, 7.9 and 8.1 (units = 10-4 dL/kg/min/mU/mL).7  Thus, insulin sensitivity was ~40% higher in the morning compared with the afternoon or evening.  It is possible that consuming a lower percentage of daily energy during the times of day when insulin sensitivity is highest (consumption of a low-energy breakfast or breakfast skipping) has an adverse impact on the cardiometabolic risk profile, increasing risks for both type 2 diabetes mellitus and atherosclerotic CVD, although prospective trials will be needed to investigate this possibility.8


  1. Uzhova I, Fuster V, Fernandez-Ortiz A, et al. The Importance of Breakfast in Atherosclerosis Disease: Insights From the PESA Study. J Am Coll Cardiol. 2017;70(15):1833-1842.
  2. Timlin MT, Pereira MA. Breakfast frequency and quality in the etiology of adult obesity and chronic diseases. Nutr Rev. 2007;65(6 Pt 1):268-281.
  3. O'Neil CE, Byrd-Bredbenner C, Hayes D, Jana L, Klinger SE, Stephenson-Martin S. The role of breakfast in health: definition and criteria for a quality breakfast. J Acad Nutr Diet. 2014;114(12 Suppl):S8-S26.
  4. Nishiyama M, Muto T, Minakawa T, Shibata T. The combined unhealthy behaviors of breakfast skipping and smoking are associated with the prevalence of diabetes mellitus. Tohoku J Exp Med. 2009;218(4):259-264.
  5. van der Heijden AA, Hu FB, Rimm EB, van Dam RM. A prospective study of breakfast consumption and weight gain among U.S. men. Obesity (Silver Spring, Md). 2007;15(10):2463-2469.
  6. Smith TJ, Dotson LE, Young AJ, et al. Eating patterns and leisure-time exercise among active duty military personnel: comparison to the Healthy People objectives. J Acad Nutr Diet. 2013;113(7):907-919.
  7. Saad A, Dalla Man C, et al. Diurnal pattern to insulin secretion and insulin action in healthy individuals. Diabetes. 2012;61(11):2691-2700.
  8. Maki KC, Phillips-Eakley AK, Smith KN. The effects of breakfast consumption and composition on metabolic wellness with a focus on carbohydrate metabolism. Adv Nutr. 2016;7 (Suppl 6):613S-621S.


Photo by Brooke Lark

Mendelian Randomization – Nature’s Clinical Trial – is Providing New Insights About the Causes and Potential Treatments for Cardiometabolic Diseases

Mendelian Randomization

Mendelian Randomization – Nature’s Clinical Trial – is Providing New Insights About the Causes and Potential Treatments for Cardiometabolic Diseases

By Kevin C. Maki, PhD

In a recent issue of JAMA Cardiology, Lyall and colleagues1 report that a score based on 97 genetic variants related to body mass index (BMI) was associated with increased risks for hypertension [odds ratio (OR) per 1-SD higher genetically-driven BMI of 1.64, 95% confidence interval (CI) 1.48-1.83], type 2 diabetes mellitus (OR 2.53; 95% CI 2.04-3.13) and coronary heart disease (CHD; OR 1.35; 95% CI 1.09-1.69).  Notably, the genetic BMI score was not associated with stroke risk.

Because the genetic score provides a measure of exposure over a lifetime to genetic variants that increase BMI, it is a relatively unconfounded marker that is less likely to be influenced by reverse causality than BMI itself.  Genotypes are assigned randomly when passed from parents to offspring during meiosis.2 The population genotype distribution should therefore be unrelated to the distribution of confounding variables.2  Accordingly, Mendelian randomization can be thought of as experiments of nature, similar to what is accomplished through randomization in a clinical trial.  The new results from Lyall et al.1 add evidence to support a causal relationship between increased BMI and cardiometabolic diseases.

Results reported in another recent paper by Dale and colleagues3 using Mendelian randomization also suggest causal roles for abdominal (waist-hip ratio adjusted for BMI; WHRadjBMI) and total adiposity (BMI) regarding risks for CHD and type 2 diabetes mellitus.  Each 1-SD higher WHRadjBMI (about 0.08 U) was associated with an excess risk of CHD (OR 1.48; 95% CI 1.28-1.71), similar to findings for BMI (SD about 4.6 kg/m2; OR 1.36; 95% CI, 1.22-1.52). WHRadjBMI, but not BMI, was associated with higher risk of ischemic stroke (OR 1.32; 95% CI, 1.03-1.70).  For type 2 diabetes mellitus, both variables had significant associations: OR 1.82 (95% CI 1.38-2.42) per 1-SD higher WHRadjBMI and OR 1.98 (95% CI 1.41-2.78) per 1-SD higher BMI.  These results are consistent with those reported by Lyall et al.1

Prior studies using Mendelian randomization have provided evidence for and against causality for several potentially modifiable risk factors for cardiometabolic diseases.  Evidence for causality has been provided for various lipoprotein-related variables and risks for atherosclerotic cardiovascular disease, including:4

  • Low-density lipoprotein cholesterol;
  • Triglycerides and triglyceride-rich lipoprotein cholesterol;
  • Lipoprotein (a).

Evidence against direct causality has been produced through Mendelian randomization for:4

  • High-density lipoprotein cholesterol;
  • C-reactive protein.

However, it should be noted that for high-density lipoprotein cholesterol and C-reactive protein, lack of association should not be interpreted to mean that these are not important risk indicators, only that the levels of these variables likely reflect other processes that are more directly involved in causal pathways.

The real promise of Mendelian randomization is to identify novel, modifiable targets for which new therapies can be developed.  This process was nicely illustrated by the identification of proprotein convertase subtilisin kexin type 9 (PCSK9) variants as predictors of CHD risk5, which ultimately led to the development of a new class of pharmaceuticals, the PCSK9 inhibitors.6


  1. Lyall DM, Celis-Morales C, Ward J, et al. Association of body mass index with cardiometabolic disease in the UK Biobank: a Mendelian randomization study. JAMA Cardiol. July 5, 2017 [Epub ahead of print].
  2. Thanassoulis G, O’Donnell CJ. Mendelian randomization: nature’s randomized trial in the post-genome era. JAMA. 2009;301:2386-2387.
  3. Dale CE, Fatemifar G, Palmer TM, et al. Causal associations of adiposity and body fat distribution with coronary heart disease, stroke subtypes, and type 2 diabetes mellitus: a Mendelian randomization study. Circulation. 2017;135:2373-2388.
  4. Lacey B, Herrington WH, Preiss D, Lewington S, Armitage J. The role of emerging risk factors in cardiovascular outcomes. Curr Atheroscler Rep. 2017;19:28.
  5. Cohen JC, Boerwinkle E, Mosley TH, Jr., Hobbs HH. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N Engl J Med. 2006;354:1264-1272.
  6. Durairaj A, Sabates A, Nieves J, et al. Proprotein convertase subtilisin/kexin type 9 (PCSK9) and its inhibitors: a review of physiology, biology, and clinical data. Curr Treat Options Cardio Med. 2017;19:58.
Mendelian Randomization