Waist Height Ratio
Introduction
Section titled “Introduction”The Waist Height Ratio (WHtR) is a simple anthropometric index calculated by dividing waist circumference by height. It serves as a practical and accessible measure for assessing central adiposity, which refers to the accumulation of fat around the abdomen. Unlike Body Mass Index (BMI), which reflects overall body weight relative to height, WHtR specifically focuses on fat distribution, particularly visceral fat, which is metabolically active and associated with various health risks. Its straightforward calculation and interpretation make it a valuable tool in both clinical and public health settings.
Biological Basis
Section titled “Biological Basis”The biological significance of WHtR stems from the distinct metabolic activity of abdominal fat. Visceral fat, located deep within the abdominal cavity around internal organs, is known to be more metabolically detrimental than subcutaneous fat (fat just under the skin). This type of fat releases inflammatory markers, free fatty acids, and hormones that can contribute to insulin resistance, dyslipidemia, and chronic inflammation. Genetic factors play a significant role in determining an individual’s fat distribution and susceptibility to central adiposity. Research in childhood obesity, for instance, has explored genetic variants linked to various anthropometric traits and body composition measures, including fat mass and trunk fat mass, highlighting the heritable nature of these characteristics.[1] Standardized anthropometric measurements are crucial for accurate assessment of these traits.[2]
Clinical Relevance
Section titled “Clinical Relevance”Clinically, WHtR is recognized as a powerful predictor of health risks associated with obesity, often outperforming BMI in identifying individuals at higher risk for certain conditions. An elevated WHtR indicates increased central adiposity, which is strongly linked to a higher incidence of cardiovascular diseases, type 2 diabetes, metabolic syndrome, and certain cancers. Childhood obesity, characterized by excess body fat, has been shown to be genetically correlated with serious comorbidities such as glucose intolerance, hypertension, dyslipidemia, insulin resistance, chronic inflammation, and an increased risk for fatty liver disease.[3], [4] Therefore, monitoring WHtR can help clinicians identify individuals who may benefit from early intervention strategies to mitigate these health complications. While BMI is a common measure, more direct assessments of adiposity are often considered more effectual in understanding the underlying biological processes.[5]
Social Importance
Section titled “Social Importance”From a societal perspective, WHtR offers several advantages. Its ease of measurement, requiring only a tape measure, makes it a cost-effective and readily applicable screening tool in diverse populations and settings, from schools to community health programs. Promoting awareness of WHtR can empower individuals to better understand their own health risks and encourage healthier lifestyle choices. Public health campaigns can utilize WHtR as an accessible metric to educate communities about the dangers of central obesity and encourage preventative behaviors. Its simplicity allows for broad application in health education and risk assessment initiatives globally.
Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”The study, while valuable, was conducted on a relatively modest sample size of 815 children from 263 Hispanic families for a Genome-Wide Association Study (GWAS).[1]This limited sample size inherently constrains statistical power, which may lead to an underestimation of the true number of genetic variants influencing complex anthropometric traits, including those potentially underlying waist height ratio. The authors acknowledge that the lack of genome-wide significant findings for some known obesity genes might be a direct consequence of sample size and statistical power, suggesting that other relevant genetic contributions could remain undetected . The precise mechanisms are still under investigation, but variations in genes involved in basic cellular functions often contribute to the complex inheritance patterns of obesity and body fat distribution.[1]Beyond protein-coding genes, non-coding RNAs also play significant regulatory roles in metabolism and body composition.LINC02161 is classified as a long intergenic non-coding RNA (lncRNA), meaning it does not code for proteins but instead functions as a crucial regulator of gene expression through various mechanisms, including chromatin modification and transcriptional control. Similarly, MIR3660 is a microRNA (miRNA), a small non-coding RNA that silences gene expression post-transcriptionally by binding to messenger RNA molecules. A genetic variant like rs10514310 , situated in a region encompassing these non-coding RNAs, could affect their transcription, processing, stability, or their ability to interact with target genes. Such disruptions might lead to altered expression of genes involved in metabolic pathways, lipid metabolism, or inflammatory responses, all of which are critical determinants of body composition and fat distribution.[1]For example, modified regulation of adipogenesis or insulin sensitivity by these non-coding RNAs could contribute to differences in central adiposity, a key factor reflected by the waist height ratio, which is a robust indicator of metabolic health risks.[1]
Causes
Section titled “Causes”The waist-height ratio, an indicator of central adiposity and overall body fat distribution, is a complex trait influenced by a multifaceted interplay of genetic, environmental, and developmental factors. Understanding its etiology requires examining how inherent biological predispositions interact with external lifestyle choices and physiological processes throughout development.
Genetic Architecture of Adiposity and Growth
Section titled “Genetic Architecture of Adiposity and Growth”Genetic factors play a substantial role in determining an individual’s waist-height ratio, with a significant polygenic component underlying common forms of obesity. Genome-wide association studies (GWAS) have identified numerous genetic loci associated with anthropometric traits and body composition, including those relevant to central fat accumulation. For instance, a nonsynonymous SNP,rs1056513 , in the INADLgene on chromosome 1 has been linked to various adiposity measures, including trunk fat mass and hip circumference, which directly contribute to the waist-height ratio.[1] Other variants, such as an intronic variant in COL4A1 on chromosome 13, are associated with weight changes, while a variant in the 5’ UTR region of TSEN34 on chromosome 19 influences linear growth, thereby affecting overall height.[1] Beyond these, specific genes involved in energy balance and metabolism, such as MTNR1Baffecting fasting glucose andAPOA5-ZNF259influencing triglycerides, also contribute to the genetic landscape of obesity and related traits.[1]While rare Mendelian forms of obesity exist, often involving genes likeCRHR1, CRHR2, MCHR1, MC3R, MC4R, and POMC, common obesity is typically polygenic, involving multiple genes with smaller individual effects.[6]
Lifestyle, Metabolic Regulation, and Energy Balance
Section titled “Lifestyle, Metabolic Regulation, and Energy Balance”Environmental factors, particularly lifestyle choices related to diet, physical activity, and sleep, significantly impact the waist-height ratio. A permissive environment characterized by readily available high-calorie foods and prevalent sedentary behaviors contributes to increased adiposity.[6] Specific genetic variants can influence these behaviors; for example, a variant in RPL7P3is associated with light physical activity, whileCTCFLis linked to sedentary-light physical activity.[1] Dietary intake is also influenced by genetic factors, with a variant in TMEM229B associated with dinner caloric intake.[1]Energy expenditure, a critical component of weight regulation, also has genetic underpinnings; variants inCHRNA3are associated with sleep energy expenditure, andMATKis linked to total energy expenditure.[1] Sleep patterns, influenced by genes like ARHGAP11Afor sleep duration, represent another environmental factor that can affect weight and body composition.[1]These environmental and behavioral elements interact with an individual’s genetic predisposition to determine their overall energy balance and, consequently, their waist-height ratio.
Gene-Environment Interactions and Developmental Influences
Section titled “Gene-Environment Interactions and Developmental Influences”The development of an elevated waist-height ratio is often a result of intricate gene-environment interactions, where genetic predispositions are amplified or mitigated by environmental exposures. For instance, a genetic tendency towards efficient energy storage can lead to obesity when combined with a modern environment abundant in food and opportunities for sedentary behavior.[6]This interaction is particularly relevant during developmental periods, such as childhood and adolescence, when body composition undergoes rapid changes. Epigenetic mechanisms, which involve modifications to gene expression without altering the underlying DNA sequence, also play a role. For example, theCTCFLgene, associated with sedentary-light physical activity, forms methylation-sensitive insulators that are involved in epigenetic regulation, potentially influencing gene activity related to fat distribution.[1]These developmental and epigenetic factors can lay the groundwork for later-life body composition, shaping an individual’s susceptibility to central adiposity.
Comorbidities and Physiological Pathways
Section titled “Comorbidities and Physiological Pathways”The waist-height ratio is also influenced by and correlated with various comorbidities and underlying physiological dysregulations. Childhood obesity, a major determinant of waist-height ratio, is genetically correlated with conditions such as glucose intolerance, hypertension, dyslipidemia, insulin resistance, chronic inflammation, and an increased risk for fatty liver disease.[3]These metabolic comorbidities reflect systemic physiological imbalances that can contribute to increased central adiposity. For example, genetic variants influencing fasting glucose (MTNR1B) or triglycerides (APOA5-ZNF259) are indicative of metabolic pathways that, when disrupted, can lead to fat accumulation, particularly in the abdominal region.[1]Furthermore, age-related changes are implicitly considered, as studies often adjust for age and sex, recognizing their impact on body composition and growth trajectories across childhood and adolescence.
Biological Background of Waist Height Ratio
Section titled “Biological Background of Waist Height Ratio”Waist height ratio is an anthropometric measure used to assess abdominal fat distribution, a key indicator of metabolic health. While the researchs primarily focuses on childhood obesity and related body composition traits like fat mass (FM), fat-free mass (FFM), trunk fat mass, and hip circumference, the underlying biological mechanisms influencing these components are directly relevant to the waist height ratio. This ratio reflects the complex interplay of genetic predispositions, metabolic regulation, cellular processes, and systemic inflammation that dictate how the body stores and utilizes energy, ultimately shaping body shape and composition.
Genetic Regulation of Adiposity and Body Structure
Section titled “Genetic Regulation of Adiposity and Body Structure”Genetic factors play a significant role in determining an individual’s body composition and fat distribution. For instance, a nonsynonymous single nucleotide polymorphism (SNP),rs1056513 , in the _INADL_ gene, located on chromosome 1, has been associated with various anthropometric traits, including weight, BMI, fat mass, trunk fat mass, and hip circumference in Hispanic children.[1] The _INADL_ gene encodes a PDZ domain-containing protein believed to be involved in the formation of tight junctions and the differentiation of adipocytes, which are fat-storing cells.[1] This suggests that variations in _INADL_could influence how and where fat is deposited, directly impacting abdominal adiposity and, consequently, the waist height ratio.
Beyond fat storage, other genetic variants influence the structural components of the body that contribute to overall size and shape. An intronic variant in_COL4A1_, a gene encoding a basement membrane collagen, was significantly associated with changes in weight z-score over time.[1] Collagens are crucial structural proteins forming the extracellular matrix, providing scaffolding for tissues and organs, including adipose tissue. Therefore, variations in _COL4A1_ may affect tissue integrity and remodeling, influencing changes in body mass and composition. Similarly, a variant in the 5’ untranslated region (UTR) of _TSEN34_, involved in tRNA splicing—a fundamental process for cell growth and division—was linked to changes in height.[1]These genetic influences on both fat deposition and skeletal growth contribute to the complex determination of an individual’s waist height ratio.
Metabolic Pathways and Energy Homeostasis
Section titled “Metabolic Pathways and Energy Homeostasis”The body’s ability to maintain energy balance and regulate metabolism is central to body composition and fat accumulation. Obesity is often viewed as a complex condition resulting from an interaction between a genetic predisposition for efficient energy storage and an environment conducive to excess food intake and sedentary behaviors.[6] Several genes have been identified that modulate key metabolic processes. For example, an intronic variant, rs10830963 , in the _MTNR1B_gene, which encodes a melatonin receptor, is strongly associated with fasting glucose levels.[1] Melatonin, the ligand for _MTNR1B_, exerts an inhibitory effect on insulin secretion, leading to elevated fasting glucose levels, a critical aspect of glucose intolerance often linked to central adiposity.
Furthermore, lipid metabolism, particularly triglyceride levels, is influenced by genetic variants in the_APOA5-ZNF259_ region.[1] _APOA5_is a significant determinant of circulating triglyceride levels, and its variants are associated with altered triglyceride concentrations, which are biomarkers for metabolic syndrome and increased cardiovascular risk.[1]Energy expenditure and substrate utilization also have genetic underpinnings. An intronic SNP in_C21orf34_was linked to respiratory quotient (RQ) during sleep, indicating genetic influences on the body’s preferential use of carbohydrates versus fats for energy.[1] Genes like _CHRNA3_, encoding a nicotinic acetylcholine receptor, impact sleep energy expenditure by influencing neural pathways that regulate energy intake and expenditure, such as proopiomelanocortin neurons and melanocortin-4 receptors.[1] Additionally, sleep duration, which is increasingly recognized as affecting weight, is associated with an intronic SNP in _ARHGAP11A_, a gene that encodes a Rho GTPase activating protein.[1]These genetic pathways collectively highlight the intricate molecular and cellular mechanisms governing energy balance, nutrient partitioning, and fat storage, all of which contribute to the waist height ratio.
Systemic Inflammation and Associated Pathophysiology
Section titled “Systemic Inflammation and Associated Pathophysiology”Obesity, particularly abdominal obesity, is often accompanied by a state of chronic low-grade inflammation, which contributes to various comorbidities.[1] This inflammatory response involves key biomolecules and signaling pathways. A nonsynonymous SNP, rs12075 , in the _DARC_gene (Duffy antigen receptor for chemokines) is strongly associated with circulating levels of monocyte chemoattractant protein-1 (_MCP-1_), a major proinflammatory cytokine.[7] _DARC_ on erythrocytes acts as a chemokine receptor and reservoir for _MCP-1_, influencing its availability in circulation.[8] Other genetic variants linked to _MCP-1_ include those in _GREB1_, _DFNB31_, _RASGEF1A_, and _CCR3_.[1] The _CCR3_gene, part of a cytokine receptor gene cluster with_CCR2_, shows increased expression in both subcutaneous and visceral adipose tissue in obese individuals, underscoring its role in adipose tissue inflammation.[9] Furthermore, variants in the _ABO_ gene, which determines blood group, have been associated with fasting serum interleukin-6 (_IL-6_) levels, another key inflammatory marker.[1]The presence of chronic inflammation is a known risk factor for conditions such as glucose intolerance, hypertension, dyslipidemia, insulin resistance, and fatty liver disease, all of which are frequently observed alongside central obesity and are thus relevant to the pathophysiological consequences of an elevated waist height ratio.
Cellular and Developmental Processes in Body Growth
Section titled “Cellular and Developmental Processes in Body Growth”The development of body size and composition is rooted in fundamental cellular processes that govern growth, differentiation, and tissue maintenance. The_TSEN34_ gene, associated with linear growth (height change), plays a critical role in tRNA splicing, a process essential for the maturation of transfer RNA molecules.[1]Mature tRNAs are indispensable for protein synthesis, which in turn drives cell growth, proliferation, and ultimately, the overall development and growth of an organism. Disruptions in such fundamental cellular machinery can therefore have systemic consequences on skeletal development and overall body size, influencing the denominator of the waist height ratio.
Moreover, the integrity and function of various tissues are maintained through the continuous synthesis and remodeling of structural components. The _COL4A1_ gene, encoding a basement membrane collagen, is involved in forming the crucial extracellular scaffold that supports cell layers and tissues throughout the body.[1]Its association with weight z-score change highlights its relevance to overall body mass and the structural aspects that underlie body composition. The_INADL_ gene, linked to adipocyte differentiation and tight junction formation, illustrates how cellular functions at the tissue level contribute to fat accumulation and distribution.[1]These processes, from molecular signaling pathways to cellular differentiation and tissue organization, collectively shape the developmental trajectory of body composition and fat distribution, which are ultimately reflected in the waist height ratio.
Risk Assessment and Comorbidity Associations
Section titled “Risk Assessment and Comorbidity Associations”The assessment of anthropometric indicators, such as body mass index (BMI), fat mass, and trunk fat mass, is fundamental in identifying individuals at risk for obesity and its associated health complications, particularly within pediatric populations. Research consistently demonstrates that childhood obesity is genetically correlated with a range of serious comorbidities, including glucose intolerance, hypertension, dyslipidemia, insulin resistance, chronic inflammation, and an elevated risk for fatty liver disease.[3], [4] These measures thus serve as crucial diagnostic utilities in identifying individuals susceptible to adverse metabolic health outcomes.
Furthermore, genetic insights contribute significantly to understanding individual susceptibility to these comorbidities. For example, a nonsynonymous single nucleotide polymorphism (SNP) inINADL, rs1056513 , has been identified with near genome-wide significance for its association with weight, BMI, fat mass, and trunk fat mass, particularly in Hispanic children.[1]Such genetic variants underscore the heritable component of body composition and adiposity, providing a deeper understanding of the underlying factors that contribute to the development of obesity and its related health conditions.
Prognostic Value and Monitoring Strategies
Section titled “Prognostic Value and Monitoring Strategies”The comprehensive assessment of body composition and related metabolic traits provides substantial prognostic value in predicting the progression of disease and long-term health trajectories. Genetic studies reveal specific variants that influence key metabolic indicators; an intronic variant inMTNR1Bis strongly associated with fasting glucose, while variants within theAPOA5-ZNF259region are linked to triglyceride levels.[1] These genetic associations highlight pathways involved in metabolic regulation and suggest inherent predispositions to certain health outcomes.
Understanding these genetic predispositions can significantly enhance monitoring strategies. Identifying children with genetic variants linked to altered glucose and triglyceride levels, which are common metabolic consequences of obesity, allows clinicians to pinpoint those at higher risk for developing more severe and chronic metabolic complications over time.[1], [10], [11]This early identification enables more targeted and timely interventions, potentially altering the disease course and improving long-term patient care.
Personalized Medicine and Prevention
Section titled “Personalized Medicine and Prevention”The identification of specific genetic loci associated with anthropometric traits and metabolic dysregulation offers a powerful tool for risk stratification and the development of personalized medicine approaches. Studies in Hispanic children have revealed genetic variants influencing various measures of body composition, including weight and BMI, providing a foundation for identifying high-risk individuals beyond traditional anthropometric screening methods.[1]Such genetic information can refine risk assessments, allowing for a more nuanced understanding of an individual’s predisposition to obesity and its complications.
Leveraging these genetic insights can guide the implementation of personalized prevention strategies. By recognizing an individual’s unique genetic susceptibility to obesity and related metabolic conditions, interventions can be specifically tailored to their genetic profile, potentially leading to more effective prevention programs and targeted treatments. This personalized approach holds promise for mitigating the progression of conditions like glucose intolerance and dyslipidemia in genetically predisposed populations, thereby improving public health outcomes.
Genetic Epidemiology of Anthropometric Traits in Diverse Populations
Section titled “Genetic Epidemiology of Anthropometric Traits in Diverse Populations”Large-scale cohort studies are fundamental for elucidating the genetic epidemiology of anthropometric traits that contribute to measures like waist height ratio. TheVIVA LA FAMILIAstudy, a notable example, conducted a genome-wide association study (GWAS) in 815 children from 263 Hispanic families to identify novel genetic loci associated with obesity-related anthropometric and body composition traits (.[1]). This cross-population comparison revealed a nonsynonymous single nucleotide polymorphism (SNP),rs1056513 , in the _INADL_ gene, which achieved near genome-wide significance for its association with weight, BMI, fat mass, trunk fat mass, fat-free mass, and hip circumference (.[1]). Such findings are critical for understanding population-specific genetic architectures of body composition and how they contribute to the prevalence patterns of obesity in diverse ethnic groups.
Longitudinal Patterns of Growth and Body Composition
Section titled “Longitudinal Patterns of Growth and Body Composition”Understanding the temporal patterns of anthropometric traits requires longitudinal studies that track changes over time, offering insights into the dynamic nature of growth and body composition. TheVIVA LA FAMILIAstudy employed a cross-sectional and longitudinal design, including a one-year follow-up to monitor children’s growth and changes in body composition, such as fat mass and fat-free mass (.[1] ). This longitudinal data enabled the identification of genetic variants associated with growth dynamics; specifically, an intronic variant in _COL4A1_ was linked to changes in weight z-score, and a 5’UTR variant in _TSEN34_ was associated with linear height change (.[1]). Such studies are invaluable for dissecting the genetic influences on growth trajectories from childhood through adolescence, which are key determinants of adult body size and composition.
Methodological Approaches and Generalizability in Genetic Research
Section titled “Methodological Approaches and Generalizability in Genetic Research”The rigor of study methodologies significantly impacts the representativeness and generalizability of population-level findings in genetic research on anthropometric traits. The VIVA LA FAMILIA study utilized a family-based GWAS approach, performing measured genotype analysis (MGA) with the SOLAR program, where phenotypes were inverse-normalized and adjusted for demographic factors like age, sex, and their interactions (.[1] ). This methodology, employing variance-components mixed models, effectively accounted for the complex pedigree structure of the Hispanic families, minimizing the confounding effects of population stratification (.[1] ). While this design is powerful for detecting genetic associations within the studied population, the generalizability of identified loci requires confirmation through replication in independent cohorts from different ancestral backgrounds to establish their broader relevance to global populations.
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs6108038 | FAM110A | waist height ratio |
| rs10514310 | LINC02161 - MIR3660 | waist height ratio |
Frequently Asked Questions About Waist Height Ratio
Section titled “Frequently Asked Questions About Waist Height Ratio”These questions address the most important and specific aspects of waist height ratio based on current genetic research.
1. Why does central fat run in my family?
Section titled “1. Why does central fat run in my family?”Yes, genetic factors play a significant role in how your body distributes fat, including a predisposition to accumulate central adiposity. This heritable nature means that if central fat is common in your family, you might have inherited a higher susceptibility to it.
2. Will my kids likely get my belly fat?
Section titled “2. Will my kids likely get my belly fat?”There’s definitely a genetic component to fat distribution and central adiposity, which your children can inherit. While lifestyle choices are very important, their genetic background might increase their susceptibility to conditions linked to excess body fat, like glucose intolerance or insulin resistance.
3. My sibling is thin but I have belly fat, why the difference?
Section titled “3. My sibling is thin but I have belly fat, why the difference?”Even with shared family genes, individual genetic variations and how they interact with lifestyle can differ. For instance, a variant likers1056513 in the INADLgene can influence body composition, but it only explains a small part of the variance. Other genetic factors and environmental exposures also contribute to these differences.
4. Does being Hispanic affect my belly fat risk?
Section titled “4. Does being Hispanic affect my belly fat risk?”Yes, research shows genetic risk factors can differ across ethnic groups. Studies on Hispanic populations have identified specific genetic influences on body composition, suggesting your background might contribute to your susceptibility to central adiposity.
5. Can healthy eating beat my family history of belly fat?
Section titled “5. Can healthy eating beat my family history of belly fat?”Yes, absolutely! While genetics influence your susceptibility, healthy lifestyle choices like diet and exercise can significantly mitigate these risks. Early intervention strategies are crucial for managing central adiposity and its associated health complications.
6. If belly fat is genetic, why bother exercising?
Section titled “6. If belly fat is genetic, why bother exercising?”Even with genetic predispositions, exercise is crucial. It helps reduce metabolically detrimental visceral fat, improving overall health and reducing risks like diabetes and heart disease, regardless of your genetic background. Your actions can significantly impact your genetic predispositions.
7. Why do some people store fat on their belly more easily?
Section titled “7. Why do some people store fat on their belly more easily?”Your genetics significantly influence your body’s fat distribution. Some individuals are genetically predisposed to accumulate more visceral fat around their organs, which is the type of fat that contributes most to a larger waist circumference and higher health risks.
8. Why does my belly fat seem harder to lose than my friend’s?
Section titled “8. Why does my belly fat seem harder to lose than my friend’s?”Genetic factors influence your metabolism and how your body stores and loses fat. If you have a genetic predisposition to central adiposity, your body might prioritize holding onto visceral fat more stubbornly, making it feel harder to lose compared to someone with a different genetic makeup.
9. Is measuring my waist actually useful for my health?
Section titled “9. Is measuring my waist actually useful for my health?”Yes, very useful! Measuring your waist height ratio is a powerful predictor of health risks like heart disease, type 2 diabetes, and metabolic syndrome, often outperforming just BMI. It helps identify central adiposity, which is metabolically detrimental.
10. Why do I get diabetes when I’m not super overweight?
Section titled “10. Why do I get diabetes when I’m not super overweight?”This is precisely why waist height ratio is so important. You might have significant visceral fat around your organs, even if your overall weight isn’t extremely high. This metabolically active fat strongly increases your risk for conditions like type 2 diabetes and chronic inflammation, regardless of your general body size.
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
Section titled “References”[1] Comuzzie, A. G. “Novel genetic loci identified for the pathophysiology of childhood obesity in the Hispanic population.”PLoS One, vol. 7, no. 12, 2012, p. e51954.
[2] Lohman, T. G., Roche, A. F., and Martorell, R. Anthropometric Standardization Reference Manual. Human Kinetics, 1988.
[3] Barlow, S. E., and Dietz, W. H. “Obesity evaluation and treatment: expert committee recommendations.”Pediatrics, vol. 102, no. 3, 1998, e29.
[4] Butte, N. F., Cai, G., Cole, S. A., and Comuzzie, A. G. “VIVA LA FAMILIA Study: genetic and environmental contributions to childhood obesity and its comorbidities in the Hispanic population.”American Journal of Clinical Nutrition, vol. 84, no. 3, 2006, pp. 646–654.
[5] Muller, M. J., Bosy-Westphal, A., and Krawczak, M. “Genetic studies of common types of obesity: a critique of the current use of phenotypes.”Obesity Reviews, vol. 11, no. 9, 2010, pp. 612–627.
[6] O’Rahilly, S., and I. S. Farooqi. “Human obesity as a heritable disorder of the central control of energy balance.”International Journal of Obesity (London), vol. 32, suppl. 7, 2008, pp. S55-S61.
[7] Schnabel, Renate B., et al. “Duffy Antigen Receptor for Chemokines (Darc) Polymorphism Regulates Circulating Concentrations of Monocyte Chemoattractant Protein-1 and Other Inflammatory Mediators.”Blood, vol. 115, no. 25, 2010, pp. 5289-5299.
[8] Rull, A., et al. “Insulin Resistance, Inflammation, and Obesity: Role of Monocyte Chemoattractant Protein-1 (or CCL2) in the Regulation of Metabolism.”Mediators of Inflammation, 2010, 326580.
[9] Huber, J., et al. “CC Chemokine and CC Chemokine Receptor Profiles in Visceral and Subcutaneous Adipose Tissue Are Altered in Human Obesity.”Obesity (Silver Spring), vol. 16, no. 1, 2008, pp. 49-54.
[10] Holzapfel, C., et al. “Association of a MTNR1Bgene variant with fasting glucose and HOMA-B in children and adolescents with high BMI-SDS.”Eur J Endocrinol, vol. 164, 2011, pp. 205-212.
[11] Sarwar, N., et al. “Triglyceride-mediated pathways and coronary disease: collaborative analysis of 101 studies.”Lancet, vol. 375, 2010, pp. 1634-1639.