Carbohydrate Intake
Introduction
Section titled “Introduction”Carbohydrate intake refers to the consumption of carbohydrates, which are essential macronutrients and a primary source of energy for the human body. The way an individual’s body processes carbohydrates is a complex trait influenced by a combination of dietary habits, lifestyle, and genetic factors. Understanding the interplay between genetic predispositions and carbohydrate intake is crucial for comprehending metabolic health.
Biological Basis
Section titled “Biological Basis”The human body’s metabolism of carbohydrates involves intricate biochemical pathways, primarily centered on glucose regulation. Genetic variations can significantly impact the homeostasis of key carbohydrates, lipids, and amino acids, often by directly affecting enzymes involved in metabolite conversion or modification.[1] For instance, specific genetic variants near G6PC2-ABCB1have been associated with glucose levels, while variants inMTNR1Bare linked to insulin secretion, withMTNR1Bbeing expressed in human islets and thought to mediate melatonin’s inhibitory effect on insulin release.[2] Another gene, PANK1, which encodes pantothenate kinase, an enzyme critical for coenzyme A synthesis, has also shown association with insulin levels, and its disruption in mouse models can lead to a hypoglycemic phenotype.[2] Metabolomic studies aim to comprehensively measure endogenous metabolites, including various sugar molecules, which provide a functional readout of the body’s physiological state and can reveal how genetic variants influence these metabolic profiles. [1]
Clinical Relevance
Section titled “Clinical Relevance”The genetic influences on carbohydrate intake and metabolism have considerable clinical implications, particularly concerning metabolic disorders. Genome-wide association studies have identified numerous genetic loci associated with diabetes-related traits, including fasting plasma glucose (FPG), fasting insulin, Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), and glycated hemoglobin (HbA1c).[3] For example, a novel association has been found between the HK1gene and glycated hemoglobin levels in non-diabetic populations.[4] Furthermore, the FTOgene has been shown to influence diabetes-related metabolic traits, often in conjunction with its effects on body mass index (BMI).[4]These genetic insights can help explain individual differences in susceptibility to conditions like type 2 diabetes and the broader metabolic syndrome, which are significant risk factors for cardiovascular events.[3]
Social Importance
Section titled “Social Importance”Understanding the genetic underpinnings of carbohydrate intake and metabolism holds significant social importance, as it contributes to personalized health strategies and public health initiatives. Given the global prevalence of metabolic diseases, elucidating how genetic variants interact with dietary carbohydrate intake can inform tailored nutritional advice and preventative interventions. This knowledge can help individuals make more informed dietary choices based on their genetic predispositions, potentially leading to improved health outcomes and a reduced burden of chronic diseases on healthcare systems.
Limitations
Section titled “Limitations”Methodological and Statistical Considerations
Section titled “Methodological and Statistical Considerations”Research into carbohydrate intake is subject to various methodological and statistical constraints that can influence the robustness and generalizability of findings. The power of genome-wide association studies (GWAS) is significantly affected by sample sizes, with larger cohorts offering improved statistical power for gene discovery.[5] Efforts to enhance power, such as using ratios of metabolite concentrations to approximate enzymatic activity, have been shown to drastically reduce variance and improve p-values, but this analytical choice itself introduces a specific interpretation framework. [1] Furthermore, rigorous replication in independent populations is considered the gold standard for validating new associations, highlighting the need for consistent findings across multiple studies to confirm true genetic links [1]. [6]
The integration of data through meta-analysis across multiple studies helps to increase overall power and refine estimates of genetic effects [7]. [5] However, variations in study-specific genotyping quality control, imputation analyses, and analytical methods (e.g., use of different statistical software or adjustments for covariates) can introduce heterogeneity and potential biases in combined results [5], [7]. [8] While techniques like genomic control and principal component analysis are employed to account for population stratification, subtle residual effects can still influence association signals [9], [10]. [4]
Population Heterogeneity and Phenotype Definition
Section titled “Population Heterogeneity and Phenotype Definition”The generalizability of findings concerning carbohydrate intake is often limited by the demographic composition of study cohorts. Many large-scale genetic studies predominantly include individuals of European ancestry, which restricts the direct applicability of results to more diverse populations and may miss ancestry-specific genetic variants or effect sizes[4], [10]. [11] Although some studies incorporate multiethnic cohorts, the primary discovery phases often focus on specific ancestral groups [7]. [5]
Phenotype measurement and definition also present challenges. Accurate assessment of carbohydrate intake, or its metabolic consequences, relies on precise and standardized methodologies. Studies often exclude individuals based on specific criteria, such as non-fasting blood samples, diabetic status, or the use of lipid-lowering medications, to ensure a more homogeneous study population and reduce confounding[2], [5], [8]. [10] While these exclusions improve internal validity, they can limit the representation of broader real-world populations. Furthermore, the use of biomarker concentrations as proxies for clinical parameters, while valuable, requires careful interpretation as these proxies may not fully capture the complexity of the underlying physiological processes. [1]
Environmental Influences and Unexplained Variation
Section titled “Environmental Influences and Unexplained Variation”Genetic associations with carbohydrate intake and its metabolic outcomes are rarely isolated, often being modulated by a complex interplay of environmental factors and other genetic influences. Studies frequently adjust for known environmental confounders such as age, smoking status, body-mass index, hormone therapy, and menopausal status to isolate genetic effects[5], [10], [12]. [11] However, the full spectrum of gene-environment interactions is difficult to capture and may contribute to unexplained variation.
Despite significant advances in identifying genetic loci, a substantial portion of the heritability for many complex traits, including those related to carbohydrate metabolism, remains unexplained, often referred to as “missing heritability”.[5]This gap suggests that many genetic variants with small effects, rare variants, or complex epistatic interactions are yet to be discovered or fully understood. The fundamental challenge of GWAS remains the prioritization of associated single nucleotide polymorphisms for functional follow-up, as statistical significance does not always translate to immediate biological understanding or clinical relevance.[6] Future research needs to integrate genetic findings into a multi-factorial “metabolic story” to fully elucidate the complex pathways involved. [1]
Variants
Section titled “Variants”Genetic variations play a crucial role in modulating an individual’s metabolic responses, including how the body processes carbohydrates and maintains glucose homeostasis. Among these, variants in genes likeFGF21, NAA25, and HECTD4 are being investigated for their potential impact on metabolic health. FGF21(Fibroblast Growth Factor 21) encodes a hormone primarily produced by the liver that acts as a key regulator of glucose and lipid metabolism, enhancing insulin sensitivity and promoting energy expenditure. Thers838133 variant might influence FGF21expression or activity, thereby affecting the body’s ability to manage carbohydrate intake and prevent conditions like insulin resistance.[2] NAA25 (N-alpha-acetyltransferase 25) is a component of the NatB complex, involved in N-terminal acetylation of proteins, a modification critical for protein stability and function across numerous cellular processes, including those in metabolism. The rs11066132 variant could alter NAA25 function, indirectly influencing metabolic pathways and the efficient utilization of dietary carbohydrates. [3] Similarly, HECTD4(HECT Domain E3 Ubiquitin Protein Ligase 4) is involved in protein ubiquitination, a fundamental process that controls protein degradation and signaling, with implications for insulin signaling and glucose transport. Variants such asrs77768175 and rs144504271 could affect HECTD4’s ligase activity, potentially altering the stability of metabolic regulatory proteins and impacting the body’s response to carbohydrate consumption.
Other notable genetic variations are found in ALDH2, TRAFD1, and the regions encompassing ADAM1A and MAPKAPK5. ALDH2 (Aldehyde Dehydrogenase 2) is a mitochondrial enzyme vital for detoxifying harmful aldehydes, including those generated from alcohol metabolism and lipid peroxidation. The rs671 variant, commonly known for causing the “alcohol flush” reaction, significantly reduces ALDH2 activity, leading to acetaldehyde accumulation. [7]This accumulation can induce oxidative stress and inflammation, potentially impairing insulin sensitivity and glucose regulation, especially with high carbohydrate and alcohol intake;rs4646776 may also contribute to these effects. TRAFD1(TRAF-type zinc finger domain containing 1) is associated with immune responses and inflammatory pathways, which are increasingly recognized as contributors to insulin resistance and metabolic syndrome. Thers12231737 variant may modulate inflammatory signaling, thereby indirectly affecting metabolic health and the body’s ability to process carbohydrates efficiently. [6] The rs78069066 variant, located in a region possibly influencing ADAM1A (ADAM Metallopeptidase Domain 1A) and MAPKAPK5 (MAP Kinase-Activated Protein Kinase 5), could affect cellular stress responses and metabolic adaptation. ADAM1A is involved in protein processing, while MAPKAPK5 plays a role in cellular responses to stress and inflammation, both of which are intertwined with metabolic regulation and the physiological handling of carbohydrates.
Finally, variants in RARB, BRAP, and ACAD10also offer insights into carbohydrate metabolism.RARB(Retinoic Acid Receptor Beta) is a nuclear receptor that, upon binding retinoic acid, acts as a transcription factor to regulate gene expression involved in cell growth, differentiation, and various metabolic pathways, including lipid and glucose metabolism. Variants such asrs7619139 and rs10510554 could modify RARB’s activity, influencing metabolic programming and an individual’s response to dietary factors, including carbohydrate intake.[12] BRAP (BRCA1 Associated Protein) is known for its role in DNA repair, but it is also implicated in metabolic processes. Genetic variations like rs11066001 and rs3782886 may link cellular stress responses and genomic integrity to metabolic regulation, potentially affecting how cells respond to nutrient availability and manage carbohydrate loads.ACAD10(Acyl-CoA Dehydrogenase Family Member 10) is critical for mitochondrial beta-oxidation, specifically the breakdown of very long-chain fatty acids. Efficient fatty acid metabolism is essential for energy balance and significantly impacts glucose utilization and insulin sensitivity. Variants such asrs11066015 and rs11066008 could impair fatty acid catabolism, leading to lipid accumulation and potentially contributing to insulin resistance, particularly in individuals consuming diets high in carbohydrates.[1]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs838133 | FGF21 | homocysteine measurement energy intake cathepsin D measurement triglyceride measurement taste liking measurement |
| rs11066132 | NAA25 | body weight epilepsy fish consumption measurement angina pectoris colorectal cancer |
| rs77768175 | HECTD4 | type 2 diabetes mellitus pancreatitis carbohydrate intake measurement hypertension high density lipoprotein cholesterol measurement |
| rs671 rs4646776 | ALDH2 | body mass index erythrocyte volume mean corpuscular hemoglobin concentration mean corpuscular hemoglobin coronary artery disease |
| rs12231737 | TRAFD1 | cups of coffee per day measurement hypertension blood urea nitrogen amount carbohydrate intake measurement coffee consumption |
| rs78069066 | ADAM1A, MAPKAPK5 | tea consumption measurement hypertension blood urea nitrogen amount carbohydrate intake measurement uric acid measurement |
| rs7619139 rs10510554 | RARB | body mass index physical activity measurement, body mass index sodium measurement energy intake taste liking measurement |
| rs11066001 rs3782886 | BRAP | BMI-adjusted waist-hip ratio forced expiratory volume, body mass index Flushing epilepsy tea consumption measurement |
| rs11066015 rs11066008 | ACAD10 | esophageal carcinoma coronary artery disease BMI-adjusted waist-hip ratio myocardial infarction fish consumption measurement |
| rs144504271 | HECTD4 | cups of coffee per day measurement forced expiratory volume, body mass index Flushing alcohol consumption quality blood urea nitrogen amount |
Classification, Definition, and Terminology
Section titled “Classification, Definition, and Terminology”Defining Key Metabolic Markers of Carbohydrate Metabolism
Section titled “Defining Key Metabolic Markers of Carbohydrate Metabolism”Glucose and insulin concentrations are fundamental indicators of carbohydrate metabolism. Glucose (GLU) refers to the primary sugar in the blood, serving as the body’s main source of energy . Clinical protocols emphasize rigorous monitoring, utilizing measures such as fasting plasma glucose and insulin concentrations, along with Homeostasis Model Assessment (HOMA) to evaluate insulin resistance and beta-cell function.[13] This comprehensive assessment allows for early identification of individuals at risk and guides the implementation of interventions aimed at mitigating the progression of metabolic dysfunction.
Longitudinal monitoring of glycemia, including measures like glycated hemoglobin (HbA1c), is also vital for assessing long-term glucose control and predicting the development of type 2 diabetes.HbA1chas been recognized for its association with cardiovascular disease and mortality in adults, underscoring its utility as a marker of cardiovascular risk.[14]While specific dietary recommendations for carbohydrate intake are not detailed in the provided context, the overarching goal of these management strategies is to maintain optimal metabolic parameters, thereby reducing the burden of conditions like diabetes and dyslipidemia.
Pharmacological Therapies for Metabolic Dysregulation
Section titled “Pharmacological Therapies for Metabolic Dysregulation”Pharmacological interventions play a significant role in treating established metabolic dysregulation, particularly in the context of diabetes and dyslipidemia. Intensive blood-glucose control, achieved through medications such as sulphonylureas or insulin, has been shown to significantly impact the development and progression of long-term complications in both insulin-dependent and type 2 diabetes.[15] Dosing considerations for these agents are tailored to achieve optimal glycemic targets while minimizing side effects, although specific dosing protocols, side effects, and contraindications are not detailed within the provided context.
Beyond glucose management, certain agents can address associated lipid abnormalities. For instance, bezafibrate, a hypolipidemic agent, is known to induce pantothenate kinase, an enzyme encoded byPANK1that is critical in coenzyme A synthesis. Mouse studies have shown that chemical knockout of pantothenate kinase can result in a hypoglycemic phenotype.[2]These examples highlight the role of targeted drug therapies in managing the complex metabolic consequences influenced by carbohydrate metabolism.
Preventive Strategies and Risk Reduction
Section titled “Preventive Strategies and Risk Reduction”Primary prevention and risk reduction for metabolic conditions involve early identification of individuals predisposed to glucose intolerance and related complications. Glycated hemoglobin (HbA1c) serves as a valuable biomarker for predicting diabetes, and its association with cardiovascular disease and mortality in adults underscores its utility as a marker of cardiovascular risk.[14] Regular screening for HbA1c can therefore facilitate early intervention, particularly in non-diabetic populations, to prevent the onset or progression of adverse metabolic outcomes, although HbA1calone may not predict cardiovascular disease in nondiabetic women.[4]
Early intervention, informed by such screening, aims to mitigate the continuous worsening of metabolic risk factors. While specific preventive strategies directly targeting carbohydrate intake are not detailed in the provided context, the emphasis on identifying and managing conditions like insulin resistance and metabolic syndrome suggests a broader approach to reduce the risk of incident cardiovascular events and other long-term complications.[16]
Genetic Insights and Emerging Approaches
Section titled “Genetic Insights and Emerging Approaches”Emerging research into the genetic architecture of metabolic traits offers insights that may inform future personalized management strategies. Genome-wide association studies have identified numerous loci influencing glucose, insulin, and lipid concentrations. For instance, common variants in theG6PC2/ABCB1genomic region are associated with fasting glucose levels, and a polymorphism withinG6PC2itself is linked to fasting plasma glucose.[4] Additionally, novel associations include HK1with glycated hemoglobin in non-diabetic populations[4] and variants in MTNR1Bon chromosome 11, which are thought to mediate the inhibitory effect of melatonin on insulin secretion.[2]
Further genetic insights include common variation near MC4Rassociated with waist circumference and insulin resistance[17] and MLXIPL associated with plasma triglycerides. [18] An INS association on chromosome 10 at rs11185790 , lying in an intron of PANK1, has also been identified. [2]While these genetic findings currently serve primarily as tools for understanding disease mechanisms and risk stratification, they hold the potential for developing novel, investigational treatments or more targeted preventive approaches based on an individual’s genetic predisposition to carbohydrate-related metabolic dysregulation. The provided context does not detail specific complementary medicine approaches.
Biological Background
Section titled “Biological Background”Carbohydrate Metabolism and Energy Homeostasis
Section titled “Carbohydrate Metabolism and Energy Homeostasis”Carbohydrate intake profoundly impacts the body’s energy homeostasis through a complex network of molecular and cellular pathways. Following consumption, carbohydrates are broken down into simpler sugars, primarily glucose, which then enters the bloodstream. This glucose is a primary fuel source for cells, undergoing glycolysis—a metabolic process initiated by enzymes like hexokinase (HK1) in red blood cells—to produce ATP[19]. [20] Dysregulation in these fundamental metabolic processes, such as erythrocyte enzyme abnormalities in glycolysis, can lead to cellular energy deficits. [20] The body’s overall physiological state can be functionally read out by metabolomics, which measures endogenous metabolites, providing insight into how genetic variants influence the homeostasis of key biomolecules like carbohydrates. [1]
Beyond immediate energy production, carbohydrate metabolism is intricately linked to other energy pathways. For instance, pantothenate kinase (PANK1) is a critical enzyme in the synthesis of coenzyme A, a vital molecule involved in numerous metabolic reactions, including the beta-oxidation of fatty acids. [2] Studies have shown that PANK1is induced by hypolipidemic agents and that its chemical knockout can lead to a hypoglycemic phenotype, highlighting its crucial role in maintaining glucose balance.[2]Furthermore, the ratio of specific metabolites, such as acylcarnitines, can serve as a functional readout of enzymatic activity, reflecting the efficiency of fatty acid breakdown and thus indirectly the body’s reliance on or shift between carbohydrate and fat as energy sources.[1]
Genetic Influences on Metabolic Pathways
Section titled “Genetic Influences on Metabolic Pathways”Genetic mechanisms play a significant role in individual variations in carbohydrate metabolism and related traits. Genetic variants influencing the homeostasis of carbohydrates are often associated with larger effect sizes due to their direct involvement in metabolite conversion and modification, providing access to underlying molecular disease-causing mechanisms.[1] For example, common variants in the FTOgene are known to alter diabetes-related metabolic traits, affecting adiposity, insulin sensitivity, leptin levels, and resting metabolic rate.[21]Similarly, a polymorphism in the calpain-10 gene has been associated with elevated body mass index and glycated hemoglobin A1c levels.[22]
Specific genes coding for enzymes involved in critical metabolic steps demonstrate how genetic variation impacts carbohydrate processing. Polymorphisms in short-chain acyl-Coenzyme A dehydrogenase (SCAD) and medium-chain acyl-Coenzyme A dehydrogenase (MCAD), enzymes that initiate the beta-oxidation of fatty acids, are strongly associated with the ratios of specific acylcarnitines. [1] For instance, rs2014355 in SCAD correlates with the C3/C4 acylcarnitine ratio, and rs11161510 in MCAD with the C8/C10 acylcarnitine ratio. [1] Minor allele homozygotes for these polymorphisms often exhibit reduced enzymatic turnover, leading to altered concentrations of metabolic substrates and products, which can shift the body’s energy substrate utilization and contribute to distinct “metabotypes”. [1]
Cellular Transport and Hormonal Regulation
Section titled “Cellular Transport and Hormonal Regulation”The cellular uptake and utilization of carbohydrates are tightly regulated by transporters and hormonal signaling pathways. Glucose transport into cells is mediated by specific facilitative glucose transport proteins, with theSLC2A family playing a critical role. [23] For example, GLUT9 (SLC2A9) is a newly identified urate transporter that significantly influences serum urate concentration and excretion, and its alternative splicing can alter cellular trafficking.[23]While primarily known for urate transport, the involvement ofSLC2Aproteins in fructose metabolism suggests broader implications for carbohydrate handling.[23]
Hormones, particularly insulin, are central to regulating glucose homeostasis. Pancreatic beta-cells are responsible for insulin secretion, a process influenced by genetic variants in genes such asMTNR1B, whose receptor mediates the inhibitory effect of melatonin on insulin secretion.[2] The zinc transporter SLC30A8(ZnT8), a beta-cell-specific protein localized in insulin secretory granules, plays a crucial role in glucose-induced insulin secretion.[24] Genetic variants in genes like KCNJ11 and ABCC8, which encode subunits of the pancreatic beta-cell KATP channel, are also strongly associated with type 2 diabetes, underscoring the genetic and molecular complexity of insulin regulation.[25]
Impact on Systemic Health and Disease Etiology
Section titled “Impact on Systemic Health and Disease Etiology”Disruptions in carbohydrate intake and metabolism can lead to significant pathophysiological processes and systemic health consequences, contributing to the etiology of common multifactorial diseases. Genetic variants that influence the homeostasis of carbohydrates can impact an individual’s susceptibility to conditions like type 2 diabetes and metabolic syndrome.[1] For instance, polymorphisms within the G6PC2gene are associated with fasting plasma glucose levels, directly impacting glucose regulation[26]. [2] Similarly, a polymorphism in GCKRis linked to elevated fasting serum triacylglycerol, reduced insulinaemia, and a decreased risk of type 2 diabetes, highlighting its role in both glucose and lipid metabolism.[10]
The long-term impact of carbohydrate metabolism is reflected in markers like glycated hemoglobin (HbA1c), which measures the non-enzymatic glycosylation of proteins and provides an average glucose level over time.[27] Variations in genes like HK1have been associated with glycated hemoglobin levels even in non-diabetic populations, suggesting a broader genetic influence on glucose regulation beyond overt disease states.[4] Furthermore, genes such as CDKAL1, IGF2BP2, CDKN2A/B, HHEX, SLC30A8, and KCNJ11have been identified as susceptibility variants for type 2 diabetes, demonstrating how genetic predispositions interact with environmental factors like nutrition and lifestyle to influence metabolic phenotypes and disease risk.[28]
Pathways and Mechanisms
Section titled “Pathways and Mechanisms”Carbohydrate Absorption and Initial Metabolism
Section titled “Carbohydrate Absorption and Initial Metabolism”Carbohydrate intake initiates a cascade of metabolic processes beginning with the absorption and cellular uptake of monosaccharides. Facilitative glucose transporters, such asSLC2A9 (also known as GLUT9), play a crucial role in mediating the transport of glucose and fructose across cell membranes, thereby making these sugars available for intracellular metabolism . Intensive blood-glucose control, often achieved through dietary modifications including carbohydrate management, has been shown to significantly reduce the development and progression of long-term complications in both type 1 and type 2 diabetes.[15]Furthermore, metabolic risk factors, including those related to carbohydrate metabolism like glucose and insulin levels, worsen continuously across the spectrum of non-diabetic glucose tolerance, contributing to conditions such as insulin resistance and metabolic syndrome, which are precursors to incident cardiovascular events.[3]
Identifying individuals at high risk for these metabolic complications is crucial for prevention and personalized medicine approaches. Fasting plasma glucose and insulin concentrations, along with measures of insulin resistance, are valuable tools for predicting type 2 diabetes.[29]Genetic variants influencing the homeostasis of carbohydrates, as identified through genome-wide association studies of metabolite profiles, can provide insights into underlying molecular disease-causing mechanisms and help refine risk stratification for conditions like dyslipidemia and inflammation.[1] For instance, specific genetic loci related to metabolic syndrome pathways, such as LEPR, HNF1A, IL6R, and GCKR, have been associated with plasma C-reactive protein levels, indicating an interplay between carbohydrate metabolism, genetic predisposition, and systemic inflammation.[10]
Diagnostic Utility and Monitoring Strategies
Section titled “Diagnostic Utility and Monitoring Strategies”Carbohydrate intake plays a central role in several clinical applications, from diagnostic utility to monitoring treatment effectiveness in metabolic disorders. Glycated hemoglobin (HbA1c) is a widely accepted diagnostic tool for diabetes mellitus, with its levels reflecting average blood glucose over the preceding months.[30]Beyond diagnosis, monitoring strategies often involve regular assessment of fasting glucose, insulin, and HbA1c to evaluate the effectiveness of lifestyle interventions, including dietary carbohydrate adjustments, and pharmacological treatments in managing diabetes and preventing its complications.[15] These biomarkers also serve as crucial adjustment variables in studies assessing other health outcomes, highlighting their fundamental impact on various physiological processes, including inflammation and liver function. [6]
The direct measurement of various sugar molecules and related metabolites in serum further enhances the ability to monitor metabolic states and identify subtle dysregulations. Targeted quantitative metabolomics platforms can determine fasting serum concentrations of numerous endogenous metabolites, including nine specific sugar molecules, providing a detailed functional readout of the physiological state related to carbohydrate processing.[1]This detailed metabolic profiling, combined with genetic insights, allows for a more nuanced understanding of an individual’s carbohydrate metabolism, aiding in the selection of appropriate treatment strategies and the precise monitoring of patient responses to interventions.[1]
Genetic Influences on Carbohydrate Metabolism
Section titled “Genetic Influences on Carbohydrate Metabolism”Genetic factors significantly modulate an individual’s response to carbohydrate intake and influence various aspects of carbohydrate metabolism. Genome-wide association studies have identified genetic variants that directly impact the homeostasis of carbohydrates, leading to larger effect sizes on metabolite conversion and providing access to underlying molecular disease mechanisms.[1] For example, a novel association has been found between the HK1gene and glycated hemoglobin in non-diabetic populations, suggesting genetic predispositions to variations in glucose control even before diabetes onset.[4]Similarly, variants at multiple loci, including those related to metabolic-syndrome pathways, contribute to polygenic dyslipidemia, a condition often exacerbated by specific carbohydrate intake patterns.[5]
These genetic insights pave the way for personalized medicine approaches in managing carbohydrate intake and related metabolic risks. Understanding an individual’s genetic profile can help identify those at higher risk for adverse metabolic responses to certain diets, enabling tailored nutritional guidance and preventive strategies.[1] For instance, a null mutation in human APOC3 has been shown to confer a favorable plasma lipid profile and apparent cardioprotection, illustrating how specific genetic alterations can influence metabolic outcomes independent of, or in conjunction with, dietary factors. [31]The integration of genetic and metabolomic data offers a powerful approach to elucidate individual differences in carbohydrate processing and to develop more effective, personalized interventions for metabolic health.[1]
References
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