Bitter Non-Alcoholic Beverage Consumption
Introduction
Section titled “Introduction”The consumption of bitter non-alcoholic beverages, such as coffee, tea, and various herbal infusions, is a prevalent dietary habit across diverse populations worldwide. These beverages are integral to daily routines, social interactions, and cultural practices, contributing significantly to overall dietary intake and lifestyle. Understanding the patterns and determinants of bitter non-alcoholic beverage consumption is crucial for public health, nutritional research, and personalized health recommendations.
Biological Basis
Section titled “Biological Basis”The perception of bitterness, a fundamental taste modality, is largely determined by genetic factors. Humans possess a family of approximately 25 bitter taste receptor genes, known as the TAS2R family, which encode proteins responsible for detecting a wide array of bitter compounds. Polymorphisms within these TAS2R genes can lead to significant individual differences in sensitivity to bitter tastes, directly influencing preferences and consumption levels of bitter beverages. For instance, variations in specific TAS2Rgenes might make an individual more or less sensitive to compounds like caffeine or polyphenols found in coffee and tea, thereby shaping their likelihood of consuming these beverages. Beyond taste perception, genetic variations also play a role in the metabolism of bioactive compounds present in these beverages, such as caffeine, which can affect their physiological impact and, consequently, consumption habits.
Clinical Relevance
Section titled “Clinical Relevance”The intake of bitter non-alcoholic beverages has been extensively studied for its potential health implications, with research linking consumption to a spectrum of clinical outcomes. For example, regular consumption of coffee and tea has been associated with effects on cardiovascular health, glucose metabolism, neurological function, and the risk of certain cancers. Accurate of consumption patterns, combined with genetic insights into taste perception and metabolism, can help researchers identify individuals who may be at different risk levels for various diseases based on their beverage choices. This understanding can also inform the development of more personalized dietary guidelines and public health interventions aimed at optimizing health through dietary modifications.
Social Importance
Section titled “Social Importance”Bitter non-alcoholic beverages hold considerable social and cultural significance globally. They are often central to social rituals, hospitality, and daily routines, reflecting deeply ingrained cultural traditions and individual preferences. Variations in consumption patterns across different populations and demographic groups can highlight disparities in dietary habits, socio-economic factors, and access to specific beverages. Recognizing the genetic and environmental factors that shape these consumption patterns is vital for understanding broader societal health trends and for crafting effective public health messages that resonate with diverse communities.
Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Many genetic studies often face limitations related to their design and statistical power, which can impact the robustness and interpretability of findings for bitter non alcoholic beverage consumption. Moderate cohort sizes, for instance, are susceptible to false negative results because of insufficient statistical power, potentially leading to the oversight of genuine genetic associations.[1] The rigorous statistical thresholds, such as Bonferroni correction for genome-wide significance, necessitate extremely large sample sizes to confidently detect genetic variants that exert only small effects, meaning many findings might be considered hypothesis-generating and require further validation.[2], [3], [4] Challenges in replicating initial genetic associations are common, with studies indicating that only a fraction of reported findings are consistently reproduced across different cohorts.[1] This lack of replication can be attributed to several factors, including false positives in original studies, insufficient power leading to false negatives in replication attempts, or fundamental differences in study populations that modify gene-phenotype relationships.[1] Furthermore, the quality of genotype imputation, which predicts unmeasured genetic variants, is critical; while studies typically exclude SNPs with low imputation scores, this process can still introduce uncertainty or inadvertently miss true associations.[5]The inherent nature of genome-wide association studies (GWAS) to use a subset of all possible SNPs also means that the current genomic coverage might be incomplete, potentially overlooking certain genes or causal variants relevant to bitter non alcoholic beverage consumption.[4]
Generalizability and Phenotypic Heterogeneity
Section titled “Generalizability and Phenotypic Heterogeneity”A significant limitation in the generalizability of genetic findings stems from the demographic composition of many study cohorts, which are predominantly comprised of individuals of European descent, often in middle-aged to elderly populations.[1] This demographic narrowness restricts the applicability of the findings to younger individuals or those from different ethnic and racial backgrounds.[1] Although researchers employ methods like principal component analysis to correct for population stratification.[6] residual confounding from subtle population substructure can still lead to inflated false positive rates or mask true genetic signals. This issue is compounded by the possibility of varying genetic architectures or allele frequencies across diverse ancestral groups, implying that variants identified in one population may not have the same relevance or effect in another.
The accurate and consistent of complex traits like bitter non alcoholic beverage consumption is essential, yet studies often encounter variability due to methodological differences in assays and distinct demographic characteristics across populations.[5] Such heterogeneity in phenotype assessment introduces noise into the data, making it more challenging to discover robust genetic associations or to replicate findings across different investigations. Discrepancies in quality control standards for both genotyping and phenotype analysis further contribute to this variability, complicating direct comparisons and meta-analyses, and potentially reducing their overall power.
Unraveling Complex Genetic Architectures
Section titled “Unraveling Complex Genetic Architectures”Despite the identification of genetic loci associated with various traits, the complete genetic architecture of bitter non alcoholic beverage consumption remains intricate. Traits often display modest-to-high heritability, yet individual SNP associations may not reach genome-wide significance, a phenomenon sometimes referred to as “missing heritability.”.[3] This suggests that many contributing genetic variants might have very small individual effects, be rare, or participate in complex interactions not easily captured by current GWAS methodologies. The use of different analytical methods can also lead to non-overlapping lists of top associated SNPs, further complicating the interpretation of results and the precise identification of causal variants.[3] Environmental factors and gene-environment interactions are crucial in modulating genetic predispositions, yet their full impact is often not completely quantified. While studies typically adjust for key covariates such as age, gender, smoking, and alcohol intake.[5] the influence of other unmeasured or poorly understood environmental confounders can obscure genuine genetic signals. Moreover, the SNPs identified as associated with a trait may not be the direct causal variants themselves but rather markers in close genomic proximity (linkage disequilibrium) to an unknown causal variant.[7] The presence of multiple causal variants within the same gene or across different genes adds another layer of complexity, making the precise elucidation of biological mechanisms challenging even when robust associations are observed.[7]
Variants
Section titled “Variants”The perception and consumption of bitter non-alcoholic beverages are complex traits influenced by a variety of genetic factors, impacting metabolism, general cellular function, and direct taste perception. Genetic variants can alter how individuals process bitter compounds, influencing their sensitivity and preferences.
The cytochrome P450 (CYP) family of enzymes plays a crucial role in metabolizing various compounds, including drugs, toxins, and many dietary components that contribute to bitter tastes. Genetic variations within or near these genes can significantly alter how individuals process bitter non-alcoholic beverages. For instance, rs2472297 is located in the intergenic region between CYP1A1 and CYP1A2, both of which are involved in xenobiotic metabolism, including caffeine and other bitter compounds. Variations here may influence the expression levels of these enzymes, thus affecting the rate at which bitter substances are broken down and eliminated from the body.[2] Similarly, the AHR gene, encoding the Aryl Hydrocarbon Receptor, is a key transcription factor regulating the expression of CYP enzymes, including CYP1A1 and CYP1A2. Variants like rs4410790 in AHR and rs7791070 in the AHR - SNORA63 intergenic region can alter AHR activity, thereby impacting the overall metabolic capacity for bitter compounds. Furthermore, POR (Cytochrome P450 Oxidoreductase) provides essential electrons to all microsomal CYP enzymes, and the rs1057868 variant in POR can affect this crucial function, potentially diminishing the activity of many CYP enzymes involved in processing bitter substances.[1] Another important enzyme, CYP2A6, primarily metabolizes nicotine but also processes other xenobiotics; the rs56113850 variant in CYP2A6 could contribute to individual differences in broader xenobiotic metabolism, potentially influencing responses to certain bitter compounds. These variations can lead to diverse individual sensitivities to bitterness, affecting preferences and consumption patterns of beverages like coffee or tea.
Beyond direct xenobiotic metabolism, other genetic variants influence broader metabolic pathways and cellular processes that can indirectly impact dietary habits and taste preferences. The rs35855035 variant, located in an intergenic region between MLXIPL and VPS37D, is particularly relevant as MLXIPL(also known as ChREBP) is a transcription factor critical for regulating glucose and lipid metabolism. Variations affectingMLXIPL activity could alter metabolic states, potentially influencing appetite and the brain’s reward system, which might indirectly affect the appeal of certain beverages.[8] Meanwhile, the UPB1 gene, where rs3788372 is located, is involved in the catabolism of pyrimidines, fundamental building blocks of DNA and RNA. Alterations in pyrimidine metabolism due to this variant could have systemic effects on cellular function and overall physiological balance. Similarly, PCMTD2 (rs1808056 ) contributes to protein repair mechanisms, and CSDC2 (rs9607819 ) is involved in regulating gene expression in response to cellular stress. While not directly linked to taste receptors, variations in these genes might influence general cellular health, neurotransmission, or hormonal regulation, which can subtly modulate an individual’s perception of taste and their inclination towards specific food and beverage choices, including those with bitter profiles.[2] Some genetic variants have a more direct impact on taste perception itself, thereby influencing the consumption of bitter non-alcoholic beverages. The ANXA9 gene, for example, encodes Annexin A9, a protein potentially involved in taste perception and salivary gland function. The rs12405726 variant within ANXA9 could alter the function or expression of this protein, directly affecting the sensitivity of taste buds to bitter compounds.[1] Individuals with certain genotypes at rs12405726 might experience bitter tastes more intensely or less intensely, leading to distinct preferences for or avoidance of beverages like black coffee, unsweetened tea, or bitter tonics. Such variations in taste sensitivity are a primary driver of individual differences in dietary choices and can significantly shape an individual’s enjoyment and consumption patterns of bitter non-alcoholic beverages.[2]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs2472297 | CYP1A1 - CYP1A2 | coffee consumption, cups of coffee per day caffeine metabolite coffee consumption glomerular filtration rate serum creatinine amount |
| rs4410790 | AHR | coffee consumption, cups of coffee per day caffeine metabolite coffee consumption cups of coffee per day glomerular filtration rate |
| rs1057868 | POR | protein coffee consumption platelet count X-21467 total cholesterol |
| rs7791070 | AHR - SNORA63 | cups of coffee per day bitter non-alcoholic beverage consumption |
| rs56113850 | CYP2A6 | nicotine metabolite ratio forced expiratory volume, response to bronchodilator caffeine metabolite cigarettes per day tobacco smoke exposure |
| rs9607819 | CSDC2 | bitter non-alcoholic beverage consumption |
| rs12405726 | ANXA9 | bitter non-alcoholic beverage consumption |
| rs1808056 | PCMTD2 | bitter beverage consumption bitter non-alcoholic beverage consumption |
| rs35855035 | MLXIPL - VPS37D | bitter non-alcoholic beverage consumption |
| rs3788372 | UPB1 | bitter non-alcoholic beverage consumption |
Methodological Evolution in Beverage Consumption
Section titled “Methodological Evolution in Beverage Consumption”The study of beverage consumption, encompassing various types, has progressed through standardized epidemiological approaches designed to quantify intake and assess its health implications. Historically, the of consumption in research settings has heavily relied on self-reported questionnaires, a technique that remains fundamental in many contemporary studies. For example, alcohol consumption, a frequently investigated beverage type, has been systematically quantified in research through questionnaires administered at specific ages, such as age 31 in the Northern Finnish Birth Cohort of 1966 (NFBC1966).[7] These protocols typically record consumption in absolute amounts, such as grams per day, or define intake thresholds, like one unit per week, ensuring consistent data collection across large populations.[7] The meticulousness of these methodologies is paramount for their effective application as covariates in studies exploring metabolic traits or other health outcomes.
Demographic Patterns and Epidemiological Significance of Consumption
Section titled “Demographic Patterns and Epidemiological Significance of Consumption”Epidemiological investigations consistently highlight distinct demographic patterns in beverage consumption, which are crucial for a comprehensive understanding of population health. Factors such as age, sex, ancestry, and socioeconomic status significantly influence prevalence rates and consumption behaviors. For instance, baseline characteristics reported in studies often distinguish “alcohol users” by sex and across various European ancestries, underscoring the necessity of considering these demographic stratifications in analytical frameworks.[9]Beyond simply quantifying intake, consumption data carries substantial epidemiological significance, frequently serving as a vital covariate in genome-wide association studies (GWAS) and other analyses of traits like liver enzymes, lipids, or uric acid.[5] The systematic adjustment for consumption habits, alongside other demographic variables such as age and sex, constitutes a standard practice to accurately disentangle genetic and environmental contributions to complex health conditions.
Temporal and Geographic Trends in Consumption Research
Section titled “Temporal and Geographic Trends in Consumption Research”The global epidemiology of beverage consumption is characterized by diverse geographic distributions and evolving temporal trends, primarily observed through the lens of large-scale cohort studies. Research efforts span various populations, including founder populations like the NFBC1966 in Finland, and diverse European cohorts such as the CoLaus Study in Switzerland, the InCHIANTI Study in Italy, and the LOLIPOP Study in the UK.[7] These studies meticulously track consumption patterns over extended periods, enabling researchers to identify secular trends and cohort effects that can influence long-term health trajectories. While precise long-term projections for specific beverage types remain complex, the continuous monitoring of consumption within these established cohorts is essential for understanding changing public health landscapes and for informing future public health strategies, particularly as consumption habits adapt across generations.[7]
Genetic Architecture of Metabolic Traits
Section titled “Genetic Architecture of Metabolic Traits”The genetic makeup of an individual significantly influences a wide array of metabolic traits, with single nucleotide polymorphisms (SNPs) serving as key determinants of variation. Genome-wide association studies (GWAS) have successfully identified numerous genetic loci where common variants contribute to the polygenic nature of traits like lipid concentrations and the risk of coronary artery disease.[10], [11] These genetic mechanisms involve specific gene functions and regulatory elements that modulate the expression patterns of genes critical for metabolic homeostasis. For example, variants in genes such as APOC3 have been linked to favorable plasma lipid profiles and apparent cardioprotection, while the GLUT9gene is associated with serum uric acid levels, indicating its role in uric acid production or elimination.[12], [13] The identification of such genetic markers provides insight into the underlying biological pathways that govern metabolic phenotypes.
Key Biomolecules and Their Cellular Pathways
Section titled “Key Biomolecules and Their Cellular Pathways”Cellular functions and metabolic processes are orchestrated by a complex interplay of critical biomolecules, including proteins, enzymes, and receptors. For instance, the regulation of lipid concentrations involves various lipoproteins such as high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, and triglycerides, each participating in distinct metabolic pathways.[10], [11]Enzymes and transporters, like those involved in uric acid metabolism, play crucial roles in maintaining the delicate balance of these biomolecules within the body.[2], [13], [14]Furthermore, hormones such as adiponectin, influenced by genes likeARL15, act as signaling molecules to regulate metabolic processes at a systemic level.[15] Metabolomics, the comprehensive of endogenous metabolites, provides a functional readout of these intricate cellular processes and the physiological state of the human body.[2]
Tissue-Specific and Systemic Metabolic Regulation
Section titled “Tissue-Specific and Systemic Metabolic Regulation”Metabolic processes are not confined to isolated cells but involve complex interactions across various tissues and organs, leading to systemic consequences. The liver, for example, plays a central role in lipid and glucose metabolism, and variations in plasma levels of liver enzymes can indicate disruptions in hepatic function.[5] Genetic variants influencing these liver enzyme levels underscore the organ-specific effects of regulatory networks.[5]The coordinated function of different organs is essential for maintaining overall homeostasis, where disturbances in the metabolism of key biomolecules, such as lipids or uric acid, can have far-reaching systemic impacts on health.[11], [13] These tissue interactions are fundamental to understanding the integrated biological system.
Pathophysiological Consequences of Metabolic Dysregulation
Section titled “Pathophysiological Consequences of Metabolic Dysregulation”Disruptions in molecular and cellular pathways, often influenced by genetic predispositions, can lead to pathophysiological processes and disease mechanisms. Dyslipidemia, characterized by abnormal lipid profiles (e.g., high LDL or triglycerides), is a significant risk factor for coronary artery disease, highlighting how metabolic imbalances contribute to disease development.[10], [11] Conversely, genetic variations that lead to a favorable lipid profile, such as a null mutation in APOC3, can confer cardioprotection.[12] These examples illustrate how the interplay between genetic mechanisms, biomolecule function, and cellular pathways directly impacts health outcomes, influencing the body’s homeostatic responses and its susceptibility to chronic conditions.
Large-Scale Cohort Studies and Longitudinal Insights
Section titled “Large-Scale Cohort Studies and Longitudinal Insights”Large-scale cohort studies are fundamental to understanding the population-level dynamics of various traits, including consumption behaviors, over time. For instance, the Northern Finnish Birth Cohort of 1966 (NFBC1966) collected extensive data on its members, including detailed information such as alcohol consumption, gathered through a questionnaire administered at age 31.[7]Similarly, other prominent population-based cohorts like CoLaus in Switzerland, InCHIANTI in Tuscany, Italy, and the LOLIPOP study in London, UK, have been instrumental in collecting comprehensive demographic, lifestyle, and biochemical data from thousands of participants.[5]These longitudinal studies allow researchers to identify temporal patterns and the long-term impacts of various factors on health outcomes, providing a rich resource for investigating the evolution of dietary and beverage consumption habits within a population.
These cohorts are characterized by their extensive data collection, often including biobank samples and repeated measurements, which facilitate the analysis of complex interactions between genetic predispositions, environmental factors, and lifestyle choices. For example, the LOLIPOP study, while primarily focused on metabolic traits, included data on alcohol consumption, defined as intake of one or more units per week.[5] Such detailed, repeated data collection across major population cohorts offers invaluable insights into the prevalence and incidence of specific consumption patterns and their associations with health markers across different life stages.
Cross-Population Comparisons and Ancestry Differences
Section titled “Cross-Population Comparisons and Ancestry Differences”Population studies frequently employ cross-population comparisons to identify variations in consumption patterns and their associated health implications across diverse ethnic and geographic groups. The LOLIPOP study, for instance, included both European white and Indian Asian populations from London, enabling researchers to investigate potential ancestry-specific effects.[5] Similarly, the GEMS Study conducted analyses across Turkish and Southern European, as well as Northern and Western European populations, highlighting how genetic and environmental factors might differentially influence traits among distinct ethnic groups.[9]Further examples of cross-population analysis include the Northern Finnish Birth Cohort of 1966, which represents a founder population, offering unique insights into genetic predispositions within a relatively homogenous group.[7]Studies like the TwinsUK registry and the GRAPHIC study in the UK, which focused on White European populations, alongside the Suita study in Japan, demonstrate the importance of diverse cohorts for assessing the generalizability of findings and identifying population-specific prevalence rates for various dietary and lifestyle factors.[16] These comparative approaches are crucial for understanding how demographic factors and cultural contexts might influence consumption behaviors and their health correlates.
Epidemiological Associations and Methodological Rigor
Section titled “Epidemiological Associations and Methodological Rigor”Epidemiological studies investigating consumption often leverage large datasets to discern prevalence patterns, incidence rates, and their associations with demographic and socioeconomic factors. Data collection frequently relies on self-reported questionnaires, such as those used in the NFBC1966 to ascertain alcohol consumption in grams per day.[7]To ensure robust findings, studies meticulously adjust for a range of covariates, including age, gender, body mass index (BMI), smoking status, and where relevant, other lifestyle factors like alcohol intake or hormone therapy use.[17] Methodologically, these studies emphasize rigorous quality control, particularly in genetic association studies, where processes like imputation based on HapMap data and stringent filtering for minor allele frequency and Hardy-Weinberg equilibrium are standard.[18] Sample sizes are typically substantial, often involving thousands of individuals, to enhance statistical power and the representativeness of the findings. Considerations for generalizability are paramount, with researchers often combining data from multiple cohorts through meta-analysis to validate associations and ensure broader applicability across diverse populations.[19]
Frequently Asked Questions About Bitter Non Alcoholic Beverage Consumption
Section titled “Frequently Asked Questions About Bitter Non Alcoholic Beverage Consumption”These questions address the most important and specific aspects of bitter non alcoholic beverage consumption based on current genetic research.
1. Why do my friends love coffee, but I find it too bitter?
Section titled “1. Why do my friends love coffee, but I find it too bitter?”Your sensitivity to bitterness is largely genetic. Variations in your TAS2R taste receptor genes can make you perceive compounds like those in coffee as much more bitter than others, influencing your preference. This is why some people enjoy strong black coffee while others prefer it sweetened.
2. Can drinking tea still benefit me if I hate bitter tastes?
Section titled “2. Can drinking tea still benefit me if I hate bitter tastes?”Yes, absolutely! While your genetic sensitivity to bitterness might make you prefer less bitter varieties or add sweeteners, the beneficial compounds in tea still offer health advantages. Your body can still process these compounds even if your taste perception makes you dislike the bitter notes.
3. Will my kids inherit my dislike for bitter drinks?
Section titled “3. Will my kids inherit my dislike for bitter drinks?”It’s quite possible! Taste preferences, especially for bitterness, have a strong genetic component. Your children might inherit some of your TAS2R gene variations, which could influence their sensitivity to bitter compounds and their likelihood of enjoying drinks like coffee or tea.
4. Does my family background affect how much bitter tea I drink?
Section titled “4. Does my family background affect how much bitter tea I drink?”Your ancestry can play a role. Genetic variations influencing taste perception and metabolism can differ across populations. While studies often focus on European descents, recognizing these differences is important for understanding how your background might influence your beverage choices and health.
5. Why does coffee keep me awake but my friend sleeps fine after it?
Section titled “5. Why does coffee keep me awake but my friend sleeps fine after it?”This is often due to genetic differences in how quickly your body metabolizes caffeine. Some people have variations in genes that make them process caffeine much slower, leading to a prolonged stimulating effect, while others break it down quickly and feel less impact.
6. Could a DNA test tell me which bitter drinks are best for me?
Section titled “6. Could a DNA test tell me which bitter drinks are best for me?”A DNA test could offer some insights into your genetic predisposition for bitter taste sensitivity (related to your TAS2R genes) or how you metabolize caffeine. This information could help you understand your natural preferences and how certain beverages might affect you, informing your choices.
7. Is research on coffee always true for me, even if I’m not European?
Section titled “7. Is research on coffee always true for me, even if I’m not European?”Not always directly. Much of the genetic research on bitter beverage consumption has focused on people of European descent. Genetic architectures and allele frequencies can vary across different ancestral groups, meaning findings might not apply exactly the same way to your specific background.
8. Why do I crave coffee even though I know it’s bitter?
Section titled “8. Why do I crave coffee even though I know it’s bitter?”While bitterness is a primary taste, other factors like caffeine’s stimulating effects and cultural habits can drive consumption. Your genetic makeup influences your initial taste perception, but the physiological effects and learned associations can also strongly influence your cravings, even for tastes you might perceive as bitter.
9. Do bitter drinks always affect my heart health the same way as others?
Section titled “9. Do bitter drinks always affect my heart health the same way as others?”Not necessarily. Your genetic makeup, including variations in taste perception and metabolism, can influence how you respond to bitter beverages. This individual variability means that the health impacts, like those on cardiovascular health, can differ from person to person, even with similar consumption levels.
10. Can my daily habits overcome a genetic tendency to dislike bitter flavors?
Section titled “10. Can my daily habits overcome a genetic tendency to dislike bitter flavors?”Yes, to a significant extent. While your genes influence your initial taste sensitivity, environmental factors and personal choices play a crucial role. You can develop a preference for bitter drinks over time through repeated exposure, or choose less bitter varieties, integrating them into a healthy lifestyle despite a genetic predisposition.
This FAQ was automatically generated based on current genetic research and may be updated as new information becomes available.
Disclaimer: This information is for educational purposes only and should not be used as a substitute for professional medical advice. Always consult with a healthcare provider for personalized medical guidance.
References
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[3] 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 Medical Genetics, vol. 8, 2007, p. 55.
[4] Yang, Q., et al. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Medical Genetics, vol. 8, 2007, p. 53.
[5] Yuan X, et al. “Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes.” Am J Hum Genet. 2008 Oct 10;83(4):520-8.
[6] Pare, G., et al. “Novel association of ABO histo-blood group antigen with soluble ICAM-1: results of a genome-wide association study of 6,578 women.” PLoS Genetics, vol. 4, no. 7, 2008, e1000118.
[7] Sabatti C, et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.” Nat Genet. 2008 Dec;40(12):1395-402.
[8] Sabatti, C., et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nature Genetics, vol. 41, no. 1, 2009, pp. 35-42.
[9] Ling H, et al. “Genome-wide linkage and association analyses to identify genes influencing adiponectin levels: the GEMS Study.” Obesity (Silver Spring). 2009 May;17(5):989-97.
[10] Kathiresan, Sekar, et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, vol. 40, no. 12, 2008, pp. 1417-24.
[11] Willer, Cristen J., et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nat Genet, vol. 40, no. 2, 2008, pp. 161-69.
[12] Pollin, Toni I., et al. “A null mutation in human APOC3 confers a favorable plasma lipid profile and apparent cardioprotection.” Science, vol. 322, no. 5906, 2008, pp. 1702-05.
[13] Li, Shiow, et al. “The GLUT9 gene is associated with serum uric acid levels in Sardinia and Chianti cohorts.”PLoS Genet, vol. 3, no. 11, 2007, p. e194.
[14] Kolz, Martin, et al. “Meta-analysis of 28,141 individuals identifies common variants within five new loci that influence uric acid concentrations.”PLoS Genet, vol. 5, no. 6, 2009, p. e1000504.
[15] Richards, J. Brent, et al. “A genome-wide association study reveals variants in ARL15 that influence adiponectin levels.”PLoS Genet, vol. 5, no. 12, 2009, p. e1000768.
[16] Hiura Y, et al. “Identification of genetic markers associated with high-density lipoprotein-cholesterol by genome-wide screening in a Japanese population: the Suita study.” Circ J. 2009 Apr;73(4):681-7.
[17] 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 May;82(5):1185-92.
[18] Wallace C, et al. “Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia.” Am J Hum Genet. 2008 Jan;82(1):139-49.
[19] Dehghan A, et al. “Association of three genetic loci with uric acid concentration and risk of gout: a genome-wide association study.” Lancet. 2008 Oct 4;372(9648):1256-61.