Breakfast Skipping

Background

Breakfast skipping refers to the habitual omission of the first meal of the day. This dietary behavior is prevalent across various populations globally, with rates varying by age, socioeconomic status, and cultural factors. Historically, breakfast has been considered a cornerstone of a healthy diet, often linked to energy levels and cognitive function throughout the day. However, modern lifestyles and changing dietary patterns have led to an increase in breakfast skipping, prompting scientific inquiry into its potential health implications.

Biological Basis

The human body operates on a circadian rhythm, influencing metabolic processes like glucose regulation, lipid metabolism, and energy expenditure. Consuming breakfast is thought to help synchronize these rhythms, signaling the start of the daily metabolic cycle. Skipping breakfast can disrupt this synchronization, potentially leading to alterations in insulin sensitivity and glucose tolerance. After an overnight fast, the body relies on stored energy. The absence of breakfast prolongs this fasting state, which can influence hormonal responses, including those of insulin, glucagon, and ghrelin. Studies have shown that meal timing, including breakfast consumption, can impact the expression of genes involved in metabolism. For instance, genome-wide association studies (GWAS) have identified numerous genetic loci associated with various metabolic traits, such as fasting plasma glucose and insulin sensitivity, which are fundamental to understanding how the body processes nutrients and responds to feeding patterns.^[1]Changes in metabolite profiles, including fatty acids and amino acids, can also be observed, reflecting the body’s altered metabolic state in response to prolonged fasting.^[2]

Clinical Relevance

Epidemiological studies have linked habitual breakfast skipping to several adverse health outcomes. These include an increased risk of obesity, type 2 diabetes, and metabolic syndrome. The disruption of metabolic rhythms and altered hormonal responses associated with skipping breakfast may contribute to these risks. For example, individuals who skip breakfast may experience greater post-meal glucose excursions and reduced insulin sensitivity later in the day. Furthermore, breakfast skipping has been associated with less favorable lipid profiles, including higher levels of triglycerides and lower levels of high-density lipoprotein (HDL) cholesterol, which are risk factors for cardiovascular disease.^[3]Genetic predisposition plays a significant role in an individual’s susceptibility to these conditions. For instance, common variants at multiple loci contribute to dyslipidemia, and genetic factors influence diabetes-related traits and uric acid concentrations, which are also relevant to metabolic health.^[3]

Social Importance

Breakfast skipping is a widespread dietary habit influenced by a complex interplay of social, economic, and behavioral factors. Time constraints, busy schedules, lifestyle choices, and cultural norms often contribute to the decision to forgo breakfast. Understanding the prevalence and determinants of breakfast skipping is crucial for public health initiatives aimed at promoting healthier eating patterns. Educational campaigns and interventions often target breakfast consumption due to its perceived importance in maintaining energy levels, improving concentration, and supporting overall well-being, particularly in children and adolescents. The public health implications extend beyond individual health, impacting healthcare costs and societal productivity.

Methodological and Statistical Considerations

Research into complex traits often faces inherent methodological and statistical limitations that can impact the interpretation of findings. Small sample sizes in initial studies can significantly restrict the power to discover novel genetic variants, indicating a need for larger cohorts to identify additional sequence variations . Changes in FGF21 signaling could affect satiety, food preferences, or the metabolic efficiency of nutrient utilization, thereby indirectly influencing dietary patterns such as the propensity to skip breakfast. Another gene, MLXIPL(MLX Interacting Protein Like), a basic helix-loop-helix leucine zipper transcription factor, is known to be associated with plasma triglyceride levels.^[4] These metabolic genes collectively highlight how genetic predispositions can influence the body’s response to food intake timing and composition.

Other variants, such as rs8097544 near LINC00470 and AIDAP3, rs35107470 near UBL7-DT and ARID3B, rs12693399 in ZNF804A, rs6017427 near RIMS4 and PGBD4P2, and rs6986473 near IFITM8P and RN7SKP135, are located in or near genes with diverse cellular functions. LINC00470 is a long intergenic non-coding RNA, which can regulate gene expression, while AIDAP3 is involved in apoptosis. ARID3B is a transcription factor important for development, and UBL7-DT is a pseudogene. ZNF804Ais a zinc finger protein often implicated in neurodevelopmental disorders and cognitive functions, which could indirectly affect decision-making around meal timing.RIMS4 is involved in synaptic vesicle release in neurons, suggesting a role in neural communication, and PGBD4P2 is a pseudogene. IFITM8P and RN7SKP135are both pseudogenes, whose functional implications are still being explored but may have regulatory roles. While the direct links of these specific variants to breakfast skipping require further investigation, variations in genes that influence neurological processes, stress response, or general cellular regulation can subtly contribute to individual differences in behavioral patterns and metabolic health. Genome-wide association studies frequently identify such variants that, while not immediately obvious, contribute to complex traits like diabetes-related traits or lipid concentrations.^[1]

Operational Definition and Measurement of Fasting

In studies investigating metabolic traits, “fasting” is operationally defined as a state of caloric abstinence for a specified period, typically overnight. This standardized condition is critical for accurate physiological measurements. For instance, blood samples used to determine concentrations of insulin (INS), glucose (GLU), triglycerides (TG), high-density lipoproteins (HDL), and low-density lipoproteins (LDL) are consistently drawn after an overnight fast, specifically between 0800 and 1100 h in the morning.^[5] This precise temporal window for blood collection, following an overnight fast, serves as the primary measurement approach to ensure a consistent physiological state across study participants. The adherence to this protocol is a key operational definition to minimize variability in biomarker levels attributable to recent food intake.

Classification and Exclusion Criteria Related to Fasting

Subjects are broadly classified into “fasting” and “nonfasting” categories based on their adherence to the overnight fasting protocol. This categorical approach is fundamental for maintaining the integrity of research findings, particularly in genetic association studies. Nonfasting status serves as a crucial exclusion criterion for the analysis of specific metabolic traits.^[5]For example, individuals who did not fast before blood collection were systematically excluded from analyses of lipid traits (TG, HDL, LDL) as well as glucose and insulin levels.^[5] This strict classification and application of research criteria are essential to prevent confounding by dietary factors and to ensure that observed associations reflect underlying biological or genetic influences rather than transient metabolic responses.

Terminology and Scientific Significance of Fasting

The primary terms employed in the context of metabolic trait measurement are “overnight fasting” and “nonfasting,” which denote the preparatory state of subjects prior to blood sampling.^[5]This methodological rigor holds significant scientific importance. Standardizing the fasted state minimizes interindividual variability in circulating levels of key metabolic indicators such as glucose, insulin, and various lipids, which are highly sensitive to recent food consumption.^[5]Such standardization is indispensable for the accurate measurement and subsequent genetic association analysis of these heritable risk factors, which are implicated in conditions like cardiovascular disease and type 2 diabetes.^[5] By controlling for fasting status, researchers can more reliably investigate the genetic determinants of these critical metabolic parameters.

Biological Background: Breakfast Skipping

Breakfast skipping, characterized by a prolonged overnight fast extending into the morning, significantly impacts the body’s intricate metabolic and homeostatic regulatory systems. This dietary behavior disrupts the regular rhythm of nutrient intake, forcing the body to adapt to extended periods of energy deprivation followed by caloric repletion. Such shifts can influence molecular signaling, gene expression, and overall physiological balance, potentially predisposing individuals to various metabolic dysregulations.

Metabolic Regulation and Energy Homeostasis

The human body tightly regulates glucose and energy balance, relying on complex molecular and cellular pathways to maintain homeostasis, particularly during periods of fasting and feeding. Skipping breakfast extends the fasting period, leading to an initial reliance on stored glycogen and then fat for energy. Key enzymes like hexokinase 1 (HK1), which phosphorylates glucose, are integral to glucose metabolism, and its activity can influence glycated hemoglobin levels, indicating long-term glucose control.^[6] Similarly, variations in the G6PC2gene, which encodes a glucose-6-phosphatase catalytic subunit, are associated with fasting glucose levels, highlighting genetic contributions to baseline glucose regulation.^[7]The glucokinase gene also plays a critical role, with common haplotypes altering fasting glucose and birth weight, underscoring its importance in early metabolic programming.^[8]Upon refeeding after a prolonged fast, the body’s response to nutrient intake can be altered, potentially leading to exaggerated glucose and insulin spikes, straining the system and influencing metabolic pathways involved in energy storage and utilization.

Genetic Predisposition to Metabolic Traits

Genetic mechanisms significantly influence an individual’s susceptibility to metabolic disturbances exacerbated by breakfast skipping. Genome-wide association studies (GWAS) have identified numerous loci associated with key metabolic traits. For instance, common variants at 30 loci contribute to polygenic dyslipidemia, a condition often linked to dietary patterns.^[9] Genes such as FTO(fat mass and obesity-associated gene) have been found to influence adiposity, insulin sensitivity, leptin levels, and resting metabolic rate, thereby altering diabetes-related metabolic traits via their effect on body mass index.^[10] Another notable gene, MC4R(melanocortin 4 receptor), is associated with waist circumference and insulin resistance, indicating its role in appetite regulation and energy expenditure.^[11] Furthermore, the GCKR(glucokinase regulatory protein) gene, through its polymorphismrs780094 , is associated with elevated fasting serum triacylglycerol, reduced fasting and oral glucose tolerance test-related insulinemia, and a reduced risk of type 2 diabetes, demonstrating its critical role in carbohydrate and lipid metabolism.^[12] These genetic predispositions establish a baseline metabolic profile that can interact with environmental factors like meal timing to influence health outcomes.

Lipid Metabolism and Cardiovascular Risk

Breakfast skipping can profoundly affect lipid metabolism, contributing to a higher risk of dyslipidemia and cardiovascular disease. The body’s handling of fats, including triglycerides (TG), high-density lipoprotein (HDL) cholesterol, and low-density lipoprotein (LDL) cholesterol, is tightly regulated by a network of critical proteins, enzymes, and receptors. For example, a genome-wide scan identified variation inMLXIPL(MLX interacting protein like) associated with plasma triglycerides, a key marker of cardiovascular health.^[4]Similarly, common single nucleotide polymorphisms (SNPs) inHMGCR (3-hydroxy-3-methylglutaryl-CoA reductase), the rate-limiting enzyme in cholesterol synthesis, can affect alternative splicing, thereby impacting cellular cholesterol homeostasis and plasma cholesterol levels.^[13] The molecular pathology of LCAT (lecithin-cholesterol acyltransferase) deficiency syndromes further highlights the importance of specific enzymes in maintaining healthy lipid profiles.^[14]Irregular eating patterns, like breakfast skipping, can disrupt the rhythmic regulation of these biomolecules and pathways, potentially leading to elevated triglycerides and unfavorable cholesterol profiles, which are known risk factors for coronary artery disease.^[15]

Systemic Metabolic Disruptions and Pathophysiological Processes

The cumulative effect of disrupted metabolic rhythms due to breakfast skipping can lead to systemic pathophysiological processes across various tissues and organs. Chronic alterations in glucose and lipid homeostasis can contribute to insulin resistance, where cells become less responsive to insulin’s signals, forcing the pancreas to produce more insulin and potentially leading to pancreatic beta-cell exhaustion over time. This can progress to type 2 diabetes, a condition for which genome-wide association analyses have identified specific loci.^[16]Beyond diabetes, prolonged metabolic stress can result in chronic low-grade inflammation, measured by markers like C-reactive protein (CRP).^[17]Such inflammation, coupled with dyslipidemia, accelerates the development of subclinical atherosclerosis, a precursor to cardiovascular events.^[18]These disruptions are not isolated but involve complex tissue interactions, where adipose tissue, liver, and muscle respond to altered nutrient signaling, leading to compensatory responses that, over time, can overwhelm homeostatic mechanisms and culminate in a range of metabolic disorders.

Regulation of Glucose Homeostasis and Energy Metabolism

The body maintains glucose and energy balance through intricate metabolic pathways and signaling cascades. Genetic variations in key enzymes, such asHK1(hexokinase 1), which is central to glycolysis and erythrocyte energy metabolism, can influence glycated hemoglobin levels in non-diabetic individuals.^[19] Similarly, the GCKR(glucokinase regulatory protein) plays a crucial role in regulating glucokinase activity, and specific polymorphisms are associated with elevated fasting serum triacylglycerol, altered insulinemia, and a modified risk of type 2 diabetes.^[20]These mechanisms highlight how subtle changes in glucose phosphorylation and regulation can significantly impact overall metabolic health.

Further regulatory mechanisms involve genes like G6PC2/ABCB11, where variations in this genomic region are linked to fasting glucose levels, andMTNR1B(melatonin receptor 1B), which is expressed in human islets and is believed to inhibit insulin secretion.^[7] Variants in MTNR1Bhave also been associated with glucose levels. The enzymePANK1(pantothenate kinase 1), essential for coenzyme A synthesis, is another critical component, with its genetic variants linked to insulin levels and mouse knockout studies demonstrating a hypoglycemic phenotype.^[5]Collectively, these pathways illustrate the precise control required for maintaining glucose homeostasis and energy availability.

Lipid Metabolism and Dyslipidemia Pathways

Lipid metabolism is a highly regulated process involving biosynthesis, catabolism, and transport, with dysregulation contributing to conditions like dyslipidemia. Genetic loci, including those near FADS1-FADS2(fatty acid desaturases), are implicated in the metabolism of fatty acids and influence serum metabolite profiles.^[21] The enzyme lecithin:cholesterol acyltransferase (LCAT) is fundamental to cholesterol esterification, and its deficiency syndromes reveal critical aspects of lipid pathology.^[14]These pathways are crucial for maintaining healthy lipid concentrations, which, when imbalanced, can increase the risk of cardiovascular diseases.

Extensive genetic studies have identified numerous loci that influence lipid concentrations, including those for low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides.^[3] The collective impact of common variants across 30 distinct loci contributes to polygenic dyslipidemia, demonstrating the complex genetic architecture underlying lipid disorders.^[3] These findings underscore the importance of understanding the genetic and mechanistic basis of lipid regulation for managing metabolic health.

Hormonal Signaling and Nutrient Sensing

Hormonal signaling plays a central role in sensing nutrient availability and regulating metabolic responses, often through receptor activation and intracellular cascades. The LEPR(leptin receptor), for instance, is critical for energy balance and satiety, with genetic variability in its locus impacting plasma fibrinogen levels.^[22] Similarly, HNF1A(hepatic nuclear factor 1 alpha) is a transcription factor involved in the trans-activation of the C-reactive protein promoter and plays a recognized role in pancreatic beta-cell function and maturity-onset diabetes of the young (MODY2).^[23] These elements highlight how nutrient sensing translates into cellular and systemic metabolic adjustments.

The IL6R(interleukin-6 receptor) is also associated with plasma C-reactive protein, linking inflammatory pathways to metabolic regulation.^[22] Furthermore, the PPARγ (peroxisome proliferator-activated receptor gamma) is a nuclear receptor that, through its polymorphism, has been associated with a decreased risk of type 2 diabetes.^[24] PPARγregulates the expression of genes involved in lipid and glucose metabolism, adipogenesis, and inflammatory responses, exemplifying how signaling pathways integrate environmental cues with genetic predisposition to modulate metabolic health.

Integrated Metabolic Networks and Disease Pathogenesis

Metabolic regulation involves complex systems-level integration, where various pathways crosstalk and interact hierarchically to maintain homeostasis. The interplay of genetic variants in LEPR, HNF1A, IL6R, and GCKRcollectively influences plasma C-reactive protein levels, illustrating how distinct metabolic and inflammatory pathways converge.^[22] This pathway crosstalk can lead to emergent properties, where the combined effect of multiple genetic predispositions and environmental factors manifests as complex metabolic phenotypes.

Dysregulation within these integrated networks is a hallmark of metabolic diseases. For example, genetic variants in the FTOgene influence adiposity, insulin sensitivity, leptin levels, and resting metabolic rate, demonstrating broad metabolic dysregulation that can impact diabetes-related traits via BMI.^[10]The continuous worsening of metabolic risk factors across the spectrum of non-diabetic glucose tolerance further underscores the systemic nature of metabolic dysfunction.^[1] Variants in pancreatic β-cell KATP channel subunits, KCNJ11 and ABCC8, are directly associated with type 2 diabetes, representing critical therapeutic targets within these intricate metabolic networks.^[25]

Implications for Diagnostic and Monitoring Strategies

The practice of breakfast skipping, which leads to a non-fasting state during morning hours, significantly impacts the accuracy and utility of several key metabolic biomarker assessments. Specifically, studies have demonstrated that lipid traits, including triglycerides (TG), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) cholesterol, along with glucose (GLU) and insulin (INS) levels, are highly sensitive to an individual’s fasting status. Consequently, researchers often exclude individuals who have not fasted before blood collection from analyses of these traits to ensure the reliability of the data, underscoring the critical importance of standardized fasting protocols in clinical diagnostics and the ongoing monitoring of metabolic health.^[5] This highlights that a patient’s adherence to fasting guidelines, frequently influenced by their breakfast habits, directly affects the interpretability of laboratory results and subsequent clinical management decisions.

Prognostic Value in Cardiovascular and Metabolic Health

Beyond the considerations for diagnostic accuracy, the non-fasting state resulting from breakfast skipping carries substantial prognostic value for cardiovascular and broader metabolic health outcomes. Research indicates that non-fasting triglyceride levels, in particular, serve as a significant independent predictor of adverse cardiovascular events, including myocardial infarction, ischemic heart disease, and overall mortality.^[15]This suggests that the physiological responses to a non-fasting state, rather than merely the absence of food intake, contribute to a distinct and elevated risk profile. Therefore, clinicians can consider non-fasting lipid profiles as a crucial indicator for assessing long-term disease progression and predicting potential complications in patients, especially concerning dyslipidemia and its associated cardiovascular conditions.

Guiding Personalized Prevention and Risk Stratification

Understanding the clinical relevance of breakfast skipping, primarily through its impact on non-fasting metabolic markers, is instrumental in guiding personalized prevention strategies and refining risk stratification. Identifying individuals with consistently elevated non-fasting triglycerides, which may be linked to irregular meal patterns, allows for a more precise risk assessment for conditions such as coronary artery disease and type 2 diabetes.^[15]This approach expands beyond traditional risk factors by incorporating dietary behaviors, enabling clinicians to implement targeted lifestyle modifications, such as promoting regular, balanced breakfast consumption. Such personalized interventions can serve as a primary prevention strategy, aiming to improve metabolic profiles and mitigate the long-term burden of cardiovascular and related comorbidities.

Key Variants

RS ID	Gene	Related Traits
rs8097544	LINC00470 - AIDAP3	breakfast skipping measurement type 2 diabetes mellitus body weight
rs35107470	UBL7-DT - ARID3B	caffeine metabolite measurement breakfast skipping measurement pain measurement
rs12693399	ZNF804A	breakfast skipping measurement
rs637174	FGF21 - RNU6-317P	breakfast skipping measurement saturated fatty acids to total fatty acids percentage polyunsaturated fatty acids to total fatty acids percentage fatty acid amount
rs6017427	RIMS4 - PGBD4P2	breakfast skipping measurement
rs6986473	IFITM8P - RN7SKP135	breakfast skipping measurement

References

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[12] Sparso T, Andersen G, Nielsen T, Burgdorf KS, Gjesing AP, et al. “The GCKR rs780094 polymorphism is associated with elevated fasting serum triacylglycerol, reduced fasting and OGTT-related insulinaemia, and reduced risk of type 2 diabetes.” Diabetologia, 2008.

[13] Burkhardt, Ralph, et al. “Common SNPs in HMGCR in Micronesians and Whites Associated with LDL-Cholesterol Levels Affect Alternative Splicing of Exon13.” Arterioscler Thromb Vasc Biol, vol. 28, no. 11, 2008, pp. 2078-2085.

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[18] O’Donnell, Christopher J., et al. “Genome-Wide Association Study for Subclinical Atherosclerosis in Major Arterial Territories in the NHLBI’s Framingham Heart Study.”BMC Med Genet, vol. 8, suppl. 1, 2007, p. S4.

[19] Murakami K, Piomelli S. “Identification of the cDNA for human red blood cell-specific hexokinase isozyme.” Blood, 1997.

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[21] Caspi A, Williams B, Kim-Cohen J, Craig IW, Milne BJ, et al. “Moderation of breastfeeding effects on the IQ by genetic variation in fatty acid metabolism.” Proc Natl Acad Sci U S A, 2007.

[22] Ridker PM, et al. “Loci related to metabolic-syndrome pathways including LEPR,HNF1A, IL6R, and GCKR associate with plasma C-reactive protein: the Women’s Genome Health Study.”Am J Hum Genet, 2008.

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[24] Altshuler D, et al. “The common PPARγ polymorphism associated decreased risk of type 2 diabetes.” Nat Genet, 2000.

[25] Gloyn AL, et al. “Large-scale association studies of variants in genes encoding the pancreatic b-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes.” Diabetes, 2003.