Body Shape
Body shape refers to the overall physical proportions and contours of the human body, a complex trait influenced by the distribution of adipose tissue (fat) and skeletal structure. It is a highly variable characteristic among individuals, determined by a combination of genetic predispositions and environmental factors such as diet, lifestyle, and physical activity. Understanding body shape involves various anthropometric measures, including body mass index (BMI), waist circumference (WC), height, and detailed assessments of fat distribution like subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) volumes.[1]
Biological Basis
Section titled “Biological Basis”Genetic factors play a substantial role in determining an individual’s body shape and its components. Research, particularly through Genome-Wide Association Studies (GWAS), has identified numerous genetic variants associated with different aspects of body shape. For instance, specific single nucleotide polymorphisms (SNPs) have been linked to variations in BMI and WC, such asrs110683 and rs4471028 . [1] Similarly, multiple genetic loci have been found to influence adult height [2], [3]. [4] Variants near the MC4R gene, including rs17782313 and rs17700633 , have been associated with fat mass, weight, and an increased risk of obesity.[5] The FTOgene is another notable example, with variants in this gene linked to obesity-related traits like BMI, hip circumference, and weight.[6] These genetic insights highlight the intricate biological pathways that regulate growth, metabolism, and fat storage, contributing to the diversity of human body shapes.
Clinical Relevance
Section titled “Clinical Relevance”Body shape is a critical indicator of an individual’s health status and risk for various diseases. Specific body shapes, particularly those characterized by excess adiposity or particular fat distribution patterns, are strongly associated with adverse health outcomes. High BMI is a well-established risk factor for cardiovascular disease, type 2 diabetes, and increased mortality.[1]Central adiposity, often quantified by waist circumference or visceral fat volume, is particularly concerning as visceral fat (VAT) is metabolically active and linked to a higher risk of metabolic syndrome, insulin resistance, and cardiovascular disease independently of overall BMI.[1]Monitoring changes in body shape over time, such as weight change, also provides valuable information about an individual’s health trajectory.[1]
Social Importance
Section titled “Social Importance”Beyond its biological and clinical implications, body shape holds significant social and psychological importance. Societal perceptions of body shape can profoundly influence self-esteem, body image, and mental well-being. Cultural ideals of beauty and health often dictate preferred body types, which can lead to body dissatisfaction and contribute to eating disorders or unhealthy weight management practices. Public health initiatives frequently target body shape parameters, such as BMI and waist circumference, to address the global epidemics of obesity and related non-communicable diseases. Understanding the complex interplay of genetics, environment, and social factors influencing body shape is crucial for promoting healthier lifestyles and fostering a more inclusive and body-positive society.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Research into body shape often faces significant methodological and statistical challenges that can influence the interpretation and generalizability of findings. Many studies, while large, may still operate with moderate cohort sizes, leading to insufficient statistical power to detect all genuine associations or to confidently replicate previously reported findings.[7] This can result in both false negative reports, where true associations are missed, and a phenomenon known as the “winner’s curse,” where initial effect sizes are overestimated. [3] Furthermore, the use of fixed-effects meta-analysis models, while common, may not adequately account for true biological or methodological heterogeneity between studies, potentially obscuring important variations in genetic effects across different populations or study designs. [3]
Another critical limitation stems from the scope of genomic coverage in genome-wide association studies (GWAS). Current GWAS often utilize only a subset of all known single nucleotide polymorphisms (SNPs), which can lead to missing associations with genes not well-covered by the array or with variants in linkage disequilibrium with the genotyped markers.[8]Moreover, quality control criteria in these studies frequently exclude variants affected by copy number polymorphisms or other structural variants, meaning their potential role in influencing body shape cannot be assessed.[4] This selective genotyping means that a substantial portion of the genetic architecture, particularly concerning rare variants or complex structural changes, remains unexplored, contributing to existing knowledge gaps in the genetic underpinnings of anthropometric traits.
Population Specificity and Phenotype Definition
Section titled “Population Specificity and Phenotype Definition”A major limitation in body shape research is the demographic homogeneity of study cohorts, predominantly consisting of individuals of European descent.[3]This narrow representation significantly restricts the generalizability of findings to other ethnic or racial groups, where genetic backgrounds, environmental exposures, and lifestyle factors may differ substantially. Population stratification is a recognized confounder, and while studies employ methods like principal component analysis to correct for it, these corrections are often applied within a broadly European context.[5]Consequently, associations identified may not be universally applicable, and a comprehensive understanding of body shape genetics requires more diverse cohorts.
Phenotype definition and measurement also pose challenges. While many studies involve careful anthropometric measurements, some rely on self-reported data for traits such as height and weight, which can introduce measurement error or bias. [5] Additionally, the practice of transforming raw anthropometric data into ‘corrected’ phenotypes, such as Z-scores or residuals adjusted for age, sex, and ancestry, aims to standardize the data but may inadvertently simplify or obscure complex biological variability. [3] Such transformations, while statistically useful, could impact the biological interpretability of effect sizes and the identification of variants that influence these traits in nuanced ways. Furthermore, studies often focus on middle-aged to elderly cohorts, introducing potential survival bias and limiting the applicability of findings to younger populations. [7]
Unaccounted Genetic and Environmental Complexity
Section titled “Unaccounted Genetic and Environmental Complexity”The genetic architecture of body shape is highly complex, and current research models may not fully capture all contributing factors. Many analyses primarily assume an additive mode of inheritance, testing for linear associations between genetic variants and anthropometric traits.[3] While this approach has been successful in identifying numerous loci, it may overlook the significant contributions of non-additive genetic effects, such as dominance or epistasis (gene-gene interactions), which could play crucial roles in trait variation. [3] The interplay between genes and environmental factors (gene-environment interactions) is also a major area of remaining knowledge gap, with complex and subtle influences that are difficult to fully model and assess in current study designs. [3]
Moreover, a substantial portion of the heritability for complex anthropometric traits remains unexplained by identified common genetic variants, a phenomenon often referred to as “missing heritability.” This suggests that other genetic factors, including rarer variants, structural polymorphisms, or epigenetic modifications, exert significant influence but are not adequately captured by current GWAS methodologies. [4]The quality control procedures in existing studies, designed to ensure data integrity, often explicitly remove certain types of genomic features, such as copy number polymorphisms, thereby preventing conclusions about their role in body shape.[4] Addressing these complex interactions and uncaptured genomic elements requires advanced platforms and analytical tools, representing a frontier for future research.
Variants
Section titled “Variants”Genetic variations play a significant role in shaping human physiology, including body composition and fat distribution. The_TRAP1_ (Heat Shock Protein 75) gene is crucial for maintaining mitochondrial health and cellular energy production, fundamental processes that underpin overall metabolic function. Variations like rs150717769 could potentially influence the efficiency of these mitochondrial pathways, indirectly impacting energy expenditure and fat storage, thereby contributing to differences in body shape. The_DNASE1_(Deoxyribonuclease I) gene encodes an enzyme responsible for degrading DNA, which is vital for removing cellular debris and maintaining genomic integrity. While its direct link to body shape is not immediately obvious, robust cellular maintenance and stress responses are broadly connected to metabolic health, which can influence body composition. Genetic studies have identified numerous loci associated with body mass index (BMI) and waist circumference, highlighting the complex genetic architecture of these traits.[1]
The _GRM8_gene encodes the metabotropic glutamate receptor 8, a protein predominantly found in the brain where it helps regulate neurotransmission, including pathways involved in appetite control and energy balance. Alterations caused by variants likers17867127 could influence how the brain signals hunger and satiety, or how it processes metabolic cues, potentially leading to differences in food intake or energy expenditure. Such changes can contribute to variations in body weight and fat distribution, which are key components of body shape.[5]The broader field of genome-wide association studies continues to uncover genetic links to various anthropometric traits, including those related to obesity.[6]
The _FGF12_ (Fibroblast Growth Factor 12) gene is involved in the proper development and function of the nervous system, playing a role in neuronal signaling. Genetic variations, such as rs75156321 and rs111783937 , could potentially alter these neural pathways, which are critical for regulating metabolism, appetite, and physical activity levels. Disturbances in these brain-based regulatory mechanisms can affect how the body stores and utilizes energy, thereby influencing overall body shape and fat distribution. The_MIR100HG_ gene is a host for several microRNAs, including _miR-100_, _miR-125b_, and _let-7a_, which are small molecules that finely tune gene expression throughout the body. These microRNAs are known to participate in fundamental biological processes, including cell growth, differentiation, and metabolism. [9] Specifically, microRNAs hosted by _MIR100HG_have been implicated in the regulation of adipogenesis—the formation of fat cells—and glucose homeostasis, which is how the body manages sugar levels. A variant likers17126580 could affect the expression or processing of these crucial microRNAs, potentially altering the body’s capacity to store fat or process glucose. Such genetic influences can significantly contribute to an individual’s predisposition to certain body shapes, including central adiposity or overall body mass.[10]
Finally, the _TLX1NB_ gene is situated adjacent to _TLX1_, a gene known for its role in development and cell differentiation. While _TLX1NB_ itself is less characterized, its chromosomal location suggests it may be involved in regulatory regions that influence nearby gene activity. A variant like rs7089940 within or near _TLX1NB_could potentially affect the expression of neighboring genes or influence broader developmental pathways that indirectly impact body architecture and metabolic regulation. The cumulative effect of such genetic variations, even those with subtle individual impacts, contributes to the wide spectrum of human body shapes and sizes observed in the population. These genetic associations highlight the intricate biological mechanisms underlying body composition and its regulation.[1]
Key Variants
Section titled “Key Variants”Classification, Definition, and Terminology of Body Shape
Section titled “Classification, Definition, and Terminology of Body Shape”Core Definitions and Key Anthropometric Measures
Section titled “Core Definitions and Key Anthropometric Measures”Body shape, particularly in the context of adiposity, is primarily defined and assessed through anthropometric measures such as Body Mass Index (BMI) and Waist Circumference (WC). BMI is an operational definition of body fatness, calculated as an individual’s weight in kilograms divided by the square of their height in meters (kg/m²).[10] This widely used metric serves as a surrogate measure for overall adiposity, reflecting the general proportion of weight to height. [10] Waist circumference, on the other hand, is a direct measure of abdominal adiposity, providing insight into the distribution of fat, specifically around the midsection. [1]Beyond these common measures, more precise assessments of body composition include radiographic techniques to quantify subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) volumes, as well as sagittal diameter, offering a detailed understanding of fat deposition.[1]
Clinical Classification and Diagnostic Criteria for Adiposity
Section titled “Clinical Classification and Diagnostic Criteria for Adiposity”Classification systems for body shape are largely driven by BMI thresholds, which serve as diagnostic criteria for categorizing individuals into different weight statuses with associated health risks. Individuals with a BMI of 25 kg/m² or greater are classified as overweight, while those with a BMI of 30 kg/m² or greater are considered obese.[10]These classifications are critical because obesity is a significant contributor to morbidity and mortality, increasing the risk for numerous conditions including type 2 diabetes mellitus, heart disease, metabolic syndrome, hypertension, stroke, and certain cancers.[10]Waist circumference, as a measure of central adiposity, complements BMI in assessing health risk, as excess abdominal fat is independently associated with cardiovascular disease risk factors.[1]
Methodological Approaches to Body Shape Assessment
Section titled “Methodological Approaches to Body Shape Assessment”The operational definitions and measurement approaches for body shape traits in research studies involve standardized protocols to ensure consistency and precision. Body weight and height are typically measured at multiple examination cycles to calculate BMI, with mean BMI often derived by averaging measurements across several examinations.[1] Similarly, waist circumference is precisely measured, often at the level of the umbilicus, and mean WC can be calculated from multiple assessments over time. [1] These anthropometric measures are frequently adjusted for various covariates, such as age, age-squared, sex, smoking status, and menopausal status, to account for confounding factors in analyses. [1] Advanced techniques, like computed tomography, are employed for detailed quantification of subcutaneous and visceral fat volumes, as well as waist circumference and sagittal diameter, providing comprehensive insights into body fat distribution. [1]
Biological Background of Body Shape
Section titled “Biological Background of Body Shape”Body shape, often quantified by measures like body mass index (BMI) and waist circumference, is a complex trait influenced by an intricate interplay of genetic predispositions, cellular metabolism, tissue distribution, and overall physiological regulation. These measures serve as indicators of adiposity and fat distribution, which are critical determinants of health outcomes.[10]Understanding the biological underpinnings of body shape involves exploring mechanisms from the molecular level to systemic effects.
Genetic Architecture of Adiposity
Section titled “Genetic Architecture of Adiposity”The genetic landscape significantly contributes to individual variations in body shape and adiposity, with twin and adoption studies highlighting a substantial heritable component.[10] Genome-wide association studies (GWAS) have identified specific genetic variants associated with BMI and waist circumference. A prominent example is the FTOgene, where common variants are strongly linked to adult and childhood obesity, influencing fat mass and overall weight[10]. [6] Another key gene, MC4R(Melanocortin 4 Receptor), has variants associated with fat mass, weight, waist circumference, and insulin resistance[5]. [9] Beyond these, numerous other candidate genes, such as ADIPOQ, ADRB2, BDNF, and CRP, have been investigated for their roles in regulating adiposity and body composition.[6]These genetic factors often act through complex regulatory networks, influencing gene expression patterns and contributing to individual susceptibility to obesity in response to environmental factors.
Molecular and Cellular Regulation of Adiposity
Section titled “Molecular and Cellular Regulation of Adiposity”At the molecular and cellular levels, body shape is profoundly influenced by metabolic processes and signaling pathways that govern energy balance and fat storage. Adipose tissue, composed primarily of adipocytes, is central to these processes, expanding or contracting based on energy intake and expenditure. Key biomolecules, including hormones like insulin and leptin, act on receptors to regulate satiety, metabolism, and fat deposition. For instance, theMC4R gene encodes a receptor that plays a critical role in the central regulation of appetite and energy homeostasis. [9]Cellular functions like lipogenesis (fat synthesis) and lipolysis (fat breakdown) are tightly controlled by enzymatic activities and regulatory networks, ensuring proper energy storage and release. Disruptions in these pathways, whether due to genetic variants or environmental factors, can lead to imbalances that manifest as changes in body shape and adiposity.
Tissue-Level Dynamics and Fat Distribution
Section titled “Tissue-Level Dynamics and Fat Distribution”Body shape is not solely determined by total fat mass but also by the distribution of adipose tissue throughout the body. Fat can be broadly categorized into subcutaneous fat (SAT), located just under the skin, and visceral fat (VAT), which surrounds internal organs in the abdominal cavity.[1] Waist circumference is a measure that reflects central adiposity, often correlating with higher levels of VAT. [1]These different fat depots have distinct metabolic and inflammatory profiles, with VAT being more metabolically active and associated with a higher risk of metabolic complications. Tissue interactions between adipose tissue and other organs, such as the liver and muscle, are crucial for systemic metabolic regulation. For example, adipose tissue secretes various adipokines and inflammatory mediators, likeTNF-alpha and CRP, which can influence insulin sensitivity, inflammation, and overall metabolic health.[11]
Pathophysiological Implications of Body Shape
Section titled “Pathophysiological Implications of Body Shape”Deviations from a healthy body shape, particularly in the form of overweight and obesity, are recognized as major pathophysiological processes with significant health consequences. Obesity, defined by a BMI of 30 kg/m² or higher, is a leading cause of morbidity and mortality worldwide.[10]This condition is strongly linked to an increased risk of type 2 diabetes mellitus, cardiovascular disease, hypertension, stroke, metabolic syndrome, and certain forms of cancer[10], [12]. [13]These adverse health outcomes arise from homeostatic disruptions, including chronic inflammation, insulin resistance, dyslipidemia, and a prothrombotic state, all of which can be exacerbated by excessive or poorly distributed fat.[14] Understanding these pathophysiological processes is crucial for developing strategies to mitigate the health risks associated with different body shapes.
Population Studies
Section titled “Population Studies”Longitudinal Tracking and Epidemiological Trends of Body Shape
Section titled “Longitudinal Tracking and Epidemiological Trends of Body Shape”Large-scale cohort studies have provided critical insights into the longitudinal patterns and epidemiological associations of body shape. The Framingham Heart Study, encompassing both its Original and Offspring cohorts, has extensively tracked body mass index (BMI) over multiple examinations spanning decades, from 1971 to 2001, and waist circumference (WC) over several offspring examinations.[1] These studies utilized repeated anthropometric measurements, calculating mean BMI and WC across various time points to capture long-term trends. [1]Such longitudinal data from Framingham have established obesity, characterized by measures like BMI, as an independent risk factor for cardiovascular disease over a 26-year follow-up, and have linked it to a prothrombotic state, highlighting the significant health implications of body shape.[12]
Beyond general adiposity, more precise body composition measures have been incorporated into these cohorts to refine epidemiological understanding. A subset of participants in the Framingham Offspring Multi-Detector Computed Tomography Study, for instance, underwent CT imaging to quantify subcutaneous fat (SAT) and visceral fat (VAT) volumes, demonstrating excellent inter-reader variability for these assessments.[1]Furthermore, birth cohorts like the Avon Longitudinal Study of Parents and Children (ALSPAC) and the Northern Finland 1966 birth cohort (NFBC1966) have enabled the tracking of anthropometric measures from birth through early adolescence and into adulthood.[10] This longitudinal approach has been instrumental in observing how genetic associations with traits like BMI, such as those involving the FTOgene, become evident in childhood and persist into adult obesity.[10]
Genetic Architecture and Cross-Population Variability in Body Shape
Section titled “Genetic Architecture and Cross-Population Variability in Body Shape”Genome-wide association (GWA) studies have extensively explored the genetic architecture underlying various body shape traits, including height, BMI, and waist circumference, across diverse populations. For instance, studies have identified numerous genetic loci influencing adult height, with one large cross-sectional study of 6,188 subjects of European descent in Lausanne, Switzerland, contributing to the discovery of 20 such loci.[3] Other GWA analyses have linked common variants in genes like HMGA2 to adult and childhood height, with findings drawn from cohorts such as ALSPAC, the Exeter Family Study of Childhood Health (EFSOCH), and FINRISK1997, all primarily involving individuals of European descent. [15] Similarly, the FTOgene has been consistently associated with BMI and predisposes to both childhood and adult obesity, while common variants nearMC4Rhave been linked to fat mass, weight, and obesity risk.[10]
Cross-population comparisons reveal significant variations in body shape characteristics and the genetic factors influencing them. While many large-scale GWA studies primarily focus on populations of European descent, such as those conducted in Finland, the UK, Switzerland, and the US[15] research has also explored other ethnic groups. For example, studies on the FTOgene have investigated its associations with obesity-related traits in African American, European American, Hispanic American, and Yoruba populations.[6] Furthermore, analyses of height have included diverse groups like the Old Order Amish, ARIC European Americans, and ARIC African Americans, revealing population-specific effects and the importance of considering ancestry in genetic studies. [2]These comparisons are crucial for understanding the generalizability of genetic findings and identifying potential population-specific genetic and environmental interactions shaping body composition.
Methodological Rigor and Study Design in Body Shape Research
Section titled “Methodological Rigor and Study Design in Body Shape Research”The robust understanding of body shape at a population level relies on diverse and methodologically sound study designs. Large-scale investigations frequently employ genome-wide association studies (GWAS) to identify genetic variants, often utilizing advanced genotyping platforms like the Affymetrix 100K or 500K GeneChips.[1]These studies meticulously assess phenotypes, ranging from basic anthropometric measures like height, weight, and waist circumference, sometimes averaged over multiple examinations for improved reliability, to more advanced body composition assessments such as whole-body dual-energy X-ray absorptiometry (DXA) in children or computed tomography (CT) for subcutaneous and visceral fat volumes in adults.[1] Rigorous quality control steps are paramount, involving the exclusion of SNPs with low minor allele frequencies, deviations from Hardy-Weinberg equilibrium, or poor call rates, as well as stringent sample filtering to remove individuals with low call rates, extreme heterozygosity, or ethnic outlier status. [1]
Representativeness and generalizability are key considerations in population studies of body shape. While many studies, such as those from the Framingham Heart Study, FINRISK1997, and various European panels, draw from large, well-characterized cohorts[1] careful attention is paid to the demographic composition and potential biases. For instance, some studies explicitly recruit subjects of self-reported “white” European descent or apply strict filters to exclude non-European individuals to mitigate issues of population stratification. [15] Methodological adjustments are also made to account for confounding factors like age, sex, smoking, and menopausal status, and statistical techniques are employed to address residual relatedness within samples. [1] Ethical approval from review committees and informed consent from participants, or their parents for child cohorts, are consistently obtained, ensuring the responsible conduct of these large-scale investigations. [15]
Ethical or Social Considerations
Section titled “Ethical or Social Considerations”Ethical Foundations of Genetic Research
Section titled “Ethical Foundations of Genetic Research”Studies involving genetic analysis of body shape, particularly in vulnerable populations like children, necessitate rigorous ethical oversight. Research protocols are typically reviewed and approved by local and national ethics committees, ensuring adherence to established guidelines.[15] A cornerstone of such research is informed consent, where participants (or their parents/guardians for minors) provide written agreement after understanding the study’s nature, risks, and benefits. This process is crucial for respecting individual autonomy and protecting participants from potential harm or exploitation.
The collection and analysis of genetic data related to body shape raise significant privacy concerns. While studies often emphasize anonymization or de-identification, the inherent uniqueness of genetic information means re-identification remains a theoretical, albeit challenging, possibility. Ethical debates also revolve around the scope of genetic testing, particularly if findings could reveal predispositions to conditions that might be stigmatized or lead to discrimination. Safeguarding genetic privacy is paramount to maintaining public trust and encouraging participation in research that could yield valuable health insights.
Societal Implications and Potential Discrimination
Section titled “Societal Implications and Potential Discrimination”Understanding the genetic underpinnings of body shape can have profound social implications, particularly concerning traits linked to health conditions like obesity. Societies often attach stigma to certain body types, which can lead to social marginalization, psychological distress, and even discrimination.[6]If genetic information about body shape predispositions becomes widely accessible, it could exacerbate existing health disparities by creating new forms of social stratification or by influencing access to resources based on perceived genetic risk. Addressing the societal impact requires careful consideration of how genetic insights are communicated and integrated into public health strategies.
A significant ethical concern is the potential for genetic discrimination, where individuals might face unfair treatment in areas such as employment, insurance, or social interactions based on their genetic predisposition to a particular body shape. While research contributes to understanding complex traits, the misinterpretation or misuse of genetic data could unfairly penalize individuals for factors largely beyond their control. Furthermore, socioeconomic factors often intersect with genetic predispositions, influencing lifestyle choices, access to healthy food, and opportunities for physical activity, which collectively shape an individual’s actual body shape and health outcomes, highlighting the need for comprehensive and equitable approaches.
Policy, Regulation, and Equity
Section titled “Policy, Regulation, and Equity”Effective policy and regulation are crucial for governing genetic research and the application of its findings related to body shape. This includes establishing robust genetic testing regulations to ensure accuracy, clinical utility, and the ethical provision of services. Comprehensive data protection frameworks are essential to secure sensitive genetic information, especially as large-scale population studies contribute to shared databases.[16] These regulatory measures, alongside stringent research ethics protocols, aim to maximize the societal benefits of genetic discoveries while minimizing potential harms and upholding public trust.
The pursuit of health equity and justice demands that the insights gained from genetic studies on body shape are applied in a manner that benefits all populations, not just privileged groups. This involves careful consideration of resource allocation to ensure that any interventions or preventative measures developed from genetic research are accessible and affordable for vulnerable populations. Furthermore, a global health perspective is vital, acknowledging that body shape and related health conditions, such as the diabetes epidemic, have widespread implications across diverse cultures and socioeconomic contexts.[6] Collaborative efforts and equitable distribution of knowledge and resources are necessary to address these complex challenges worldwide.
References
Section titled “References”[1] Fox, C. S. “Genome-wide association to body mass index and waist circumference: the Framingham Heart Study 100K project.”BMC Med Genet, vol. 8, suppl. 1, 2007, p. S18.
[2] Sanna, S., et al. “Common variants in the GDF5-UQCC region are associated with variation in human height.” Nature Genetics, vol. 40, no. 2, 2008, pp. 198-203.
[3] Weedon, M. N., et al. “Genome-wide association analysis identifies 20 loci that influence adult height.” Nat Genet, 2008, PMID: 18391952.
[4] Lettre, G., et al. “Identification of ten loci associated with height highlights new biological pathways in human growth.” Nat Genet, 2008, PMID: 18391950.
[5] Loos, R. J. “Common variants near MC4R are associated with fat mass, weight and risk of obesity.”Nat Genet, vol. 40, no. 6, 2008, pp. 768-71.
[6] Scuteri, A. “Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits.”PLoS Genet, vol. 3, no. 7, 2007, p. e115.
[7] Benjamin, E. J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, 2007, PMID: 17903293.
[8] Yang, Q., et al. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Med Genet, 2007, PMID: 17903294.
[9] Chambers, J. C., Elliott, P., Zabaneh, D., Zhang, W., Li, Y., Froguel, P., Balding, D., Scott, J., & Kooner, J. S. “Common genetic variation near MC4R is associated with waist circumference and insulin resistance.”Nat Genet, 2008, 40:768-775.
[10] Frayling, T. M. “A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity.”Science, vol. 316, no. 5826, 2007, pp. 889-94.
[11] Melzer, D., Perry, J. R., Hernandez, D., Corsi, A. M., Stevens, K., et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet, 2008, 4:e1000072.
[12] Hubert, H. B., Feinleib, M., McNamara, P. M., & Castelli, W. P. “Obesity as an independent risk factor for cardiovascular disease: a 26-year follow-up of participants in the Framingham Heart Study.”Circulation, 1983, 67:968-977.
[13] Calle, E. E., Thun, M. J., Petrelli, J. M., Rodriguez, C., & Heath, C. W. Jr. “Body-mass index and mortality in a prospective cohort of U.S. adults.”N Engl J Med, 1999, 341:1097-1105.
[14] Rosito, G. A., D’Agostino, R. B., Massaro, J., Lipinska, I., Mittleman, M. A., et al. “Association between obesity and a prothrombotic state: the Framing-ham Offspring Study.”Thromb Haemost, 2004, 91:683-689.
[15] Weedon, M. N. “A common variant of HMGA2 is associated with adult and childhood height in the general population.” Nat Genet, vol. 39, no. 10, 2007, pp. 1245-50.
[16] Meigs, J. B. “Genome-wide association with diabetes-related traits in the Framingham Heart Study.” BMC Med Genet, vol. 8, suppl. 1, 2007, p. S16.