D Trehalose
Background
Section titled “Background”d-Trehalose is a naturally occurring disaccharide, a type of sugar molecule, composed of two glucose units linked by an α,α-1,1-glycosidic bond. It is widely distributed in nature, found in various organisms such as bacteria, fungi, insects, and plants. In these organisms, d-trehalose primarily functions as an energy storage compound and a protective agent against diverse environmental stresses, including desiccation, extreme temperatures (heat and cold), and oxidation.
Biological Basis
Section titled “Biological Basis”Within biological systems, d-trehalose plays a crucial role in maintaining cellular integrity and function under adverse conditions. It is known for its ability to stabilize proteins and cell membranes, preventing their denaturation and aggregation when exposed to stress. Beyond its protective capabilities, d-trehalose also serves as a readily available energy source. The metabolism of carbohydrates, including d-trehalose and other related metabolites, is influenced by an individual’s genetic makeup. Research, such as genome-wide association studies (GWAS), investigates genetic variants associated with the profiles of various metabolites found in human serum, which includes carbohydrates.[1]
Clinical Relevance
Section titled “Clinical Relevance”The unique properties of d-trehalose have led to significant interest in its potential clinical applications. Its cytoprotective effects are being explored in the context of neurodegenerative diseases, such as Huntington’s and Parkinson’s diseases, where it may promote cellular clearance processes like autophagy to remove misfolded proteins. Furthermore, d-trehalose’s involvement in carbohydrate metabolism positions it as a subject of investigation for metabolic health. Genome-wide association studies have identified genetic determinants related to diabetes-related traits, including levels of glucose and glycated hemoglobin[2] and have also examined factors influencing kidney function [3] both of which are closely linked to overall metabolic health.
Social Importance
Section titled “Social Importance”d-Trehalose holds considerable social importance across several industries. In the food sector, it is valued as a functional ingredient, serving as a sweetener, a stabilizer, and a moisture-retaining agent, which helps preserve the texture and freshness of food products. Its application extends to the pharmaceutical and cosmetic industries, where its protective qualities are utilized in drug formulations and skincare products. The ongoing research into its therapeutic benefits for age-related conditions and metabolic disorders further underscores its growing significance for public health and advanced biomedical research.
Limitations
Section titled “Limitations”Methodological and Statistical Considerations
Section titled “Methodological and Statistical Considerations”The generalizability of findings from genome-wide association studies (GWAS) can be constrained by several methodological and statistical factors. Many investigations operated with moderate sample sizes, which inherently limits statistical power and raises the potential for false negative results, meaning genuine genetic associations may remain undetected. Furthermore, the reproducibility of findings across independent cohorts is often inconsistent, with only a fraction of reported associations typically confirmed in subsequent replication studies. This variability can stem from initial false positive discoveries, differences in study designs, or varying statistical power among cohorts, and can lead to discrepancies in reported effect sizes.. [4]
The reliance on a subset of available single nucleotide polymorphisms (SNPs) in GWAS, coupled with imputation analyses based on reference panels like HapMap, means that not all potential causal variants or genes may be comprehensively covered. Imputation, while extending genomic coverage, carries an inherent error rate, indicating that inferred genotypes are not always perfectly accurate, which can introduce uncertainty into association results. This incomplete genomic coverage and potential for imputation errors can restrict the thorough investigation of candidate genes and the identification of all relevant genetic loci influencing a given trait..[5]
Statistical modeling choices also present limitations; many analyses assume an additive genetic model, which may not fully capture more complex genetic architectures, such as dominant, recessive, or epistatic interactions. To mitigate the multiple testing burden, some studies opted for sex-pooled analyses, potentially overlooking sex-specific genetic effects that might manifest differently in males and females. Additionally, strategies like averaging phenotypic measurements over extended periods, while intended to reduce measurement noise, assume that the underlying genetic and environmental influences on the trait remain stable across a wide age range. This assumption may not hold true, potentially masking age-dependent gene effects or introducing misclassification due to evolving measurement techniques and equipment over time.. [5]
Generalizability and Phenotype Heterogeneity
Section titled “Generalizability and Phenotype Heterogeneity”A significant limitation in many genetic association studies is the predominant focus on cohorts of white European ancestry, which inherently restricts the generalizability of findings to other ethnic and racial groups. Genetic associations, allele frequencies, and patterns of linkage disequilibrium can vary substantially across different populations, meaning that discoveries made in one ancestral group may not be directly transferable or fully applicable to others. Although some studies include multiethnic cohorts for replication, the initial discovery phases often lack sufficient diversity, thereby limiting a comprehensive understanding of genetic architecture across the global population.. [4]
Challenges in phenotype definition and measurement consistency can also impact the reliability and interpretation of genetic associations. When traits are measured over long durations or across various study sites, inconsistencies in measurement protocols, equipment changes, or varying definitions can introduce biases and potential misclassification. For instance, the averaging of physiological traits spanning decades, while aiming to reduce short-term variability, may inadvertently obscure age-dependent genetic effects and relies on the potentially flawed assumption of consistent measurement techniques over time. Furthermore, the exclusion of participants with specific conditions or those on medications, while necessary for some study designs, can limit the applicability of the findings to broader segments of the population who might be undergoing treatment or have co-morbidities.. [6]
Unaccounted Genetic and Environmental Influences
Section titled “Unaccounted Genetic and Environmental Influences”The intricate interplay between genetic predispositions and a multitude of environmental factors is frequently not fully elucidated, leading to an incomplete understanding of the complex etiology of many traits. Assumptions regarding the constancy of genetic and environmental influences across broad age ranges, for example, may obscure critical age-dependent gene effects or nuanced gene-environment interactions that significantly contribute to phenotypic variation. While some studies have initiated gene-by-environment testing for a limited number of factors, the vast and complex array of potential environmental confounders and their interactions with genetic variants largely remains unexplored, representing a significant knowledge gap.. [6]
Despite the identification of numerous associated genetic loci, a substantial portion of the heritability for many complex traits often remains unexplained by the identified variants. The specific causal variants underlying statistically associated SNPs are frequently unknown, with identified associations often reflecting linkage disequilibrium with true causal variants rather than the genotyped SNPs themselves. Consequently, the precise functional mechanisms by which these genetic variations influence biological pathways and ultimately impact the trait require extensive follow-up research and functional validation, underscoring ongoing knowledge gaps in translating genetic associations into biological understanding.. [4]
Variants
Section titled “Variants”The TREHgene encodes trehalase, an enzyme crucial for the digestion of trehalose, a disaccharide sugar found in many foods, particularly mushrooms, yeast, and insects. Trehalase breaks down trehalose into two molecules of glucose in the small intestine, making it available for absorption and energy.TREHP1 is a pseudogene associated with TREH, often a non-functional copy that may play a role in gene regulation or represents an evolutionary remnant. Genetic variations can significantly impact enzyme activity and metabolic pathways, influencing how individuals process dietary sugars. [1] Understanding these genetic underpinnings is vital for comprehending individual differences in nutrient processing and metabolic health. [7]
The variant rs592280 , located within or near the TREH or TREHP1genes, may influence the expression levels or the enzymatic efficiency of trehalase. Such genetic alterations can affect an individual’s ability to properly digest d-trehalose. For instance, a variant leading to reduced trehalase activity could result in trehalose intolerance, manifesting as gastrointestinal symptoms like bloating, gas, and diarrhea upon consuming d-trehalose-rich foods. Genome-wide association studies frequently identify single nucleotide polymorphisms (SNPs) associated with various metabolic traits, providing insights into their biochemical mechanisms.[1] These genetic insights are important for understanding individual dietary responses and personalized nutrition. [8]
Proper d-trehalose metabolism by theTREHenzyme is essential for maintaining glucose homeostasis and overall digestive well-being. Disruptions in this process, potentially influenced by genetic variants such asrs592280 , can have broader metabolic consequences beyond simple digestion, potentially impacting nutrient absorption and general gut health. Research has shown that many genes and their variants are associated with biomarkers of cardiovascular disease and dyslipidemia, underscoring the complex genetic architecture underlying metabolic health.[8]For example, variants affecting other sugar-metabolizing enzymes or their regulators, such as the glucokinase regulator (GCKR), have been linked to various metabolic traits, demonstrating the widespread impact of genetic variations on carbohydrate metabolism.[8]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs592280 | TREH - TREHP1 | D-Trehalose measurement |
References
Section titled “References”[1] Gieger, C., et al. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.”PLoS Genet, vol. 4, no. 11, 2008, e1000282.
[2] Meigs, James B., et al. “Genome-Wide Association with Diabetes-Related Traits in the Framingham Heart Study.” BMC Medical Genetics, vol. 8, suppl. 1, 2007, p. S1.
[3] Hwang, Shih-Jen, et al. “A Genome-Wide Association for Kidney Function and Endocrine-Related Traits in the NHLBI’s Framingham Heart Study.” BMC Medical Genetics, vol. 8, suppl. 1, 2007, p. S10.
[4] Benjamin, E. J. et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, vol. 8, 2007, p. 57.
[5] Yang, Q. et al. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Med Genet, vol. 8, 2007, p. 58.
[6] Vasan, R. S. et al. “Genome-wide association of echocardiographic dimensions, brachial artery endothelial function and treadmill exercise responses in the Framingham Heart Study.”BMC Med Genet, vol. 8, 2007, p. 56.
[7] Pare, G. et al. “Novel association of HK1 with glycated hemoglobin in a non-diabetic population: a genome-wide evaluation of 14,618 participants in the Women’s Genome Health Study.”PLoS Genet, vol. 4, no. 12, 2008, e1000308.
[8] Wallace, C., et al. “Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia.”Am J Hum Genet, vol. 82, no. 1, 2008, pp. 139-149.