Rationale: Childhood asthma and obesity have reached epidemic proportions worldwide, and the latter is also contributing to increasing rates of related metabolic disorders, such as diabetes. Yet, the relationship between asthma, obesity, and abnormal lipid and glucose metabolism is not well understood, nor has it been adequately explored in children.
Objectives: To analyze the relationship between asthma diagnosis and body mass in children across the entire range of weight percentile categories, and to test the hypothesis that early derangement in lipid and glucose metabolism is independently associated with increased risk for asthma.
Methods: Cross-sectional analysis of a representative sample of public school children from a statewide community-based screening program, including a total of 17,994 children, 4 to 12 years old, living in predominantly rural West Virginia, and enrolled in kindergarten, second, or fifth grade classrooms.
Measurements and Main Results: We analyzed demographics; family history; smoke exposure; parent-reported asthma diagnosis; body mass index; evidence of acanthosis nigricans as a marker for developing insulin resistance; and fasting serum lipid profile including total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides. Regardless of their body mass index percentile, children diagnosed with asthma were more likely than children without asthma to have higher triglyceride levels and acanthosis nigricans after controlling for sex differences and smoke exposure.
Conclusions: This study provides the first set of community-based data linking asthma, body mass, and metabolic variables in children. In particular, these findings uniquely describe a statistically significant association between asthma and abnormal lipid and glucose metabolism beyond body mass index associations.
Childhood asthma and obesity have reached epidemic proportions worldwide, and the latter is also contributing to increasing rates of related metabolic disorders, such as diabetes. Yet, the relationship between asthma, obesity, and abnormal lipid and glucose metabolism is not well understood.
This study provides the first set of community-based data linking asthma, body mass, and metabolic variables in children. In particular, these findings uniquely describe a significant association between asthma and abnormal lipid and glucose metabolism beyond body mass index associations.
Previous studies of the association between asthma and obesity have focused on the mechanical effects of abdominal fat on respiratory system compliance (7, 8); on the role of specific nutrients, such as antioxidants and saturated fat (6); and on the inflammatory pathways implicated in both conditions (7, 10). Much of this literature focuses on obesity as the central hub from which complications, such as asthma, cardiovascular disease, and metabolic syndrome, originate. Perhaps as a result of this bias, most of the studies designed to examine the interactions between childhood asthma and obesity were based on select cohorts of children who are obese. Although this strategy is valuable to identify trends within an at-risk group, new and important information could result from studies looking at larger, more heterogeneous samples of children stratified by body mass. Also, an association between childhood asthma and metabolic risk factors independent of obese body mass has not been studied among children and may identify potential confounding factors.
Among the metabolic comorbidities frequently associated with obesity, dyslipidemia and hyperinsulinemia can influence both innate and adaptive defense mechanisms in the respiratory tract, thus promoting the expression of multiple proinflammatory cytokines and chemokines, reduced endogenous antiinflammatory activity, and increased bronchomotor tone (11). Because these events are involved in the pathophysiology of airway inflammation and hyperreactivity, it is conceivable that early life abnormalities in lipid or glucose metabolism may contribute to the pathogenesis of asthma in childhood.
The original goal of this project was to analyze the relationship between asthma diagnosis and body mass in a community-based sample of children across the entire range of weight percentile categories from underweight to morbidly obese. To this end, we gathered demographic data and estimates of body mass index (BMI) and asthma prevalence in a sample of more than 17,000 children of both sexes and age ranging from 4 to 12 years. Because these children were participating in a comprehensive cardiovascular risk detection screening program, we also had access to their metabolic data, including fasting serum levels of total cholesterol; low-density lipoprotein (LDL) cholesterol; high-density lipoprotein (HDL) cholesterol; triglycerides; and evidence of acanthosis nigricans (AN), a brown to black hyperpigmented skin rash used as a biomarker for developing insulin resistance and hyperinsulinemia. Analyzing metabolic variables in this large and heterogeneous pediatric population, we noted striking associations of asthma prevalence with triglyceride levels and AN, which led us to test the hypothesis that early metabolic dysfunction, and not unhealthy weight per se, is associated with airway inflammation and childhood asthma.
Additional information on the methods is provided in the online supplement.
We studied children enrolled during the 2007–2008 academic year in public schools throughout the state of West Virginia and participating in the Coronary Artery Risk Detection In Appalachian Communities (CARDIAC) Project, a community-based cardiovascular risk detection program (14, 15). All children enrolled in kindergarten, second, and fifth grade classrooms in West Virginia were eligible to participate in CARDIAC. Parental consent and child assent (for second and fifth grade students only) were required to enter this study and all procedures were approved by the West Virginia University Institutional Review Board. Before screening day, participating parents provided demographic and family history information by completing a questionnaire.
During screening day, children's height and weight were measured using a SECA Road Rod stadiometer and a SECA 840 digital scale (Seca Corp., Hanover, MD). BMI for each child was calculated using the equation recommended by the US Centers for Disease Control and Prevention:
All BMI percentile categories were based on age- and sex-specific growth charts recommended by the Centers for Disease Control and Prevention (16), and were defined as follows: underweight less than 5th percentile; healthy weight 5th to 84.9th percentile; overweight 85th to 94.9th percentile; obese 95th to 98.9th percentile; morbidly obese greater than 99th percentile.
Neck and axilla of all fifth grade students were screened for this hyperpigmented skin rash associated with insulin resistance and hyperinsulinemia in children (17). Our screening staff was trained on the use of previously validated criteria (18), and was required to report whether AN was present or absent (see online supplement).
Blood samples for fasting lipid profile were obtained from fifth grade children only and were analyzed by LabCorp Inc. (Burlington, NC). A personalized report including serum levels of total cholesterol, LDL, HDL, and triglycerides was generated for each participant.
CARDIAC screening procedures have been described in detail by previous publications (14, 15). In brief, this project screens more than 24,000 children annually in collaboration with the West Virginia Rural Health Educational Partnerships (WVRHEP). For each county, WVRHEP provides one site coordinator, one assistant coordinator, and health sciences students completing rural rotations at selected sites.
WVRHEP site coordinators and school teachers distributed consent booklets to all children enrolled in kindergarten, second grade, and fifth grade. Parents and legal guardians were asked to return the signed consent forms to their children's classroom teachers. In turn, the site coordinators collected the forms from the teachers before the scheduled screening date. Fifth grade students were screened at the beginning of screening day, because they were required to fast for the lipid profile blood draw and had to eat breakfast before going to class that morning. All information obtained from the screening was summarized in a comprehensive health report and mailed to the children's families. Each report also contained information on how to interpret the screening results, what additional services may be needed, and how to maintain a healthy lifestyle with adequate diet and physical activity.
Complete statistical information is provided in the online supplement.
We studied a sample of 17,944 children including 6,314 kindergarten students (35.2%); 5,609 second grade students (31.2%); and 6,021 fifth grade students (33.5%). Slightly less than half (49.3%) of these children were male (8,854 males vs. 9,027 females). Racial distribution of the sample was slightly more diverse than the total population of West Virginia, with 90.7% of children being white compared with 95.9% statewide. Most of the mothers (57.1%) had completed some college or obtained a college degree; 49.9% of fathers had obtained the same amount of education (Table 1).
Characteristics (n = 17,944)
Number in Sample
% of Total Sample
Males (n = 8,854) %
Females (n = 9,027) %
Sex Difference P Value
|High school or fewer years||1,956||10.9||8.6||12.7|
|High school graduate or GED||5,903||32.9||34.2||39.1|
|High school or fewer years||4,001||22.3||24.0||21.1|
|High school graduate or GED||5,132||28.6||28.0||28.9|
|Race or ethnicity|
|History of parent heart attack||5,398||30.2||49.8||50.2|
|Diagnosed with asthma||2,521||14.1||57.8||42.2||<0.01|
|Exposed to tobacco smoke||5,102||28.5||48.2||51.8|
|Body mass index percentiles|
|Normal weight, 5th–84.9th%||9,965||55.8||48.8||51.2|
| Morbidly obese, ≥99th%||1,134||6.3||57.3||42.7||<0.001|
Almost one third of the children's parents (30.2%) reported heart attacks among their first-degree relatives, and almost half reported surgical interventions for coronary disease (19.5% open heart surgery, 26.7% angioplasty). Approximately half of the sample (49.1%) reported a family history of diabetes. Also, approximately one (28.5%) out of four children had been exposed to second-hand tobacco smoke. Females were more likely to be underweight (P < 0.001), whereas males were more likely to be morbidly obese (P < 0.001). Males were also more likely to have been diagnosed with asthma (P < 0.01). We found no other relevant sex differences in demographic characteristics or family history.
The BMI percentile classification revealed that more than one-third of the children in our sample (37.6%) had a weight above healthy range, and one (20.9%) out of five children was obese or morbidly obese. Fourteen percent of the overall sample had been diagnosed with asthma by a medical provider according to their parents or legal guardians. Overall, asthma and BMI were found to be significantly correlated (P = 0.02). The percent of people with asthma within each BMI category of the total sample is illustrated in Figure 1. As a general trend, the asthma prevalence rate increased as children's BMI percentile increased. Importantly, asthma prevalence in children who were obese and morbidly obese was significantly higher than in children with healthy BMI (P < 0.001), whereas simple overweight status did not increase asthma risk (P = 0.82).
Figure 1 also illustrates asthma prevalence as it is distributed across grade and sex categories. Again, in most instances the prevalence of asthma was higher for higher BMI percentile categories. For example, 14.2% of male kindergarten students with healthy BMI had asthma compared with 22.2% of the kindergarten students who were morbidly obese male (P < 0.01), and this difference persisted into adolescence (14.9% vs. 26.4% in fifth grade; P < 0.01). Across grades, children who were obese and morbidly obese had consistently the highest percentages of asthma both for males and females (P < 0.001), and children who were simply overweight were consistently similar to those with healthy weight. In contrast, asthma patterns for children who were underweight were more variable and differed for males and females and by grade.
Table 2 and Figure 2A illustrate the mean serum levels for total cholesterol, LDL, HDL, and triglycerides distributed by body weight category. In general, obesity was associated with significantly higher levels of total cholesterol, LDL, and log-transformed triglycerides, and with lower levels of HDL (P < 0.0001 for each analysis of variance model). However, whereas the serum levels of total cholesterol and LDL increased only slightly going from overweight to obese and actually decreased going from obese to morbidly obese, triglycerides increased progressively and significantly as the BMI category increased (P < 0.001 for each category) and a sharp increase was noted between the obese and morbidly obese categories. A pattern similar to that of serum triglycerides was noted for the prevalence of AN (Figure 2B).
|Underweight BMI <5th%, n = 592||156.71||1.20||49.89||0.51||88.11||1.05||94.19||2.55|
|Healthy weight BMI 5th–84.9th%, n = 2,269||157.87||0.53||54.7||0.25||91.20||0.55||74.28||0.82|
|Overweight BMI 85th–94.9th%, n = 861||161.45||0.98||50.00||0.35||96.72||1.01||91.16||1.69|
|Obese BMI 95th–98.9th%, n = 905||165.21||0.99||45.32||0.33||100.69||0.99||118.14||2.16|
|Morbidly Obese BMI >99th%, n = 283||162.03||1.80||41.59||0.55||98.16||1.88||133.54||4.92|
Because the serum triglyceride and AN profiles mimicked closely the trends of asthma prevalence in our sample, we tested the hypothesis that triglycerides (along with the rest of the fasting lipid panel) and AN are associated with the prevalence of childhood asthma independent of weight status. Figure 3 shows the proportion of fifth grade children with high serum levels for total cholesterol, LDL, HDL, and triglycerides (Figure 3A) and the proportion of fifth grade children with evidence of AN (Figure 3B) based on the presence or absence of asthma diagnosis. A statistically significant effect was found for the overall model of asthma diagnosis (P = 0.016). But more importantly, serum triglycerides (log-transformed; P = 0.011) and AN (P = 0.006) were significantly associated with asthma regardless of weight status and after controlling for sex and smoking status, thereby supporting our hypothesis.
In addition, we examined the association between overweight or obese weight status and serum lipids or AN, independent of the prevalence of asthma. The overall model was again significant (P < 0.001) for all dependent variables including total cholesterol, HDL, LDL, log-transformed triglycerides, and AN after controlling for sex and smoking status. Finally, we tested whether asthma diagnosis and BMI interact to affect serum lipids, but in this case we did not find a significant effect for the overall model (P = 0.14). Collectively, these results suggest a strong relationship between asthma diagnosis, serum triglycerides, and AN regardless of weight; a strong relationship between weight, serum lipids, and AN regardless of asthma diagnosis; and no interaction between BMI and asthma on serum lipids.
After discovering the general trends outlined previously, we explored individual relationships between asthma diagnosis and serum lipid levels by hierarchical linear regression (Figure 4A and Table 3). The overall model was significant (adjusted R2 = 0.07; P < 0.01) and asthma was significantly associated with hypertriglyceridemia after controlling for sex, smoke exposure, and BMI percentile (ΔR2 P = 0.006). Similarly, we used binary logistic regression to explore the association between presence of AN and diagnosis of asthma (Figure 4B). The overall model was again significant (Nagelkerke R2 = 0.09; P < 0.001) and asthma was significantly associated with AN after controlling for sex, smoke exposure, and BMI percentile. Thus, the diagnosis of asthma continued to be significantly associated with both triglycerides levels and AN after controlling for sex, smoke exposure, and BMI.
|Dependent Variable||Independent Variable||β||P||Nagelkerke R2||OR|
The results of this study confirm that asthma prevalence generally increases with the children's BMI percentile. More specifically, asthma prevalence is increased among children who are obese, and even more so in children who are morbidly obese, although it is not more prevalent among children who are overweight compared with those of normal weight. This suggests that only above a certain threshold metabolic factors participate in the pathophysiology of airway inflammation and hyperreactivity, leading to the clinical manifestations of asthma and the use of health care resources culminating in the physician diagnosis of asthma. It is possible, however, that milder, subclinical abnormality in pulmonary function may already emerge in the overweight status. Our data also confirmed general age and sex differences in asthma prevalence, with older children and males being more affected.
To our knowledge, this is the first study to examine the association between physician-diagnosed asthma and weight using a large community-based sample of children with varying age, body mass, and metabolic risks. Our previous work has shown that about 50% of the eligible students participate in the CARDIAC screenings and that the differences between participants and nonparticipants are minimal (19). Nonparticipants are less likely to have a primary care provider and to have health insurance, but there is no difference in BMI or any other of the variables analyzed in the present study.
Furthermore, our data indicate that children diagnosed with asthma tend to have higher serum triglyceride levels based on age-specific reference values (20), and higher rates of insulin resistance as predicted by the AN biomarker. This association is independent of the children's sex and history of exposure to tobacco smoke. Most importantly, this association exists regardless of the children's body mass, implying that children whose weight is within or even below the healthy range may still be more susceptible to develop asthma because of subtle metabolic derangements heralded by increasing triglyceride or glucose blood levels. Also, it is possible that the reported association between obesity and asthma may have been confounded by the frequent association of obesity, hypertriglyceridemia, and insulin resistance.
Several previous studies have focused on childhood obesity to better understand the apparent clustering of chronic conditions, such as asthma, diabetes, and cardiovascular diseases (8, 21). In addition to the hypothetical relationship between obesity and asthma, there is strong evidence that obesity is associated with the development of insulin resistance and diabetes (22). In turn, diabetes and insulin resistance are associated with diminished lung function (23–25), and some studies have also found a relationship between insulin resistance and reduced lung function among nondiabetics, even after controlling for BMI (23, 26).
Based on the present data, we have formulated the hypothesis that early metabolic abnormalities induced by imbalanced diet during pregnancy and childhood constitute the central hub from which the asthma-obesity-diabetes triad originates, at least in a subpopulation of patients. To test this hypothesis, we have recently developed in parallel to our epidemiologic studies an animal model of the effects of hypercaloric diet on metabolic and airway responses in rats. Our initial data show that dietary imbalances during pregnancy and lactation are associated with inflammation and airway hyperreactivity early in life (27), suggesting a role for maternal diet as a risk factor for the development of childhood asthma. Interestingly, the primary metabolic abnormality found in the hyperreactive offspring from dams fed with high-fat diet was hypertriglyceridemia, which correlated with body fat but not with body weight (27).
The information provided by this study also confirms the disproportionate burden of environmental and metabolic risk factors in the low-income, predominantly rural populations of West Virginia and Appalachia, which makes these populations uniquely suited for studies of the pathogenesis of chronic diseases. In particular, the prevalence of cardiovascular risk factors and diabetes found in the families included in our sample is consistent with the most recent statewide epidemiologic data (complete information on the burden of diabetes in West Virginia is provided in the online supplement) and is higher than the national average (28, 29). The prevalence of tobacco smoke exposure in the home (28.5%) is also above the national average of approximately 25%. Finally, the incidence of obesity (20.9%) is higher than the national estimate based on the 2003–2006 National Health and Nutrition Examination Survey that 17% of children 6 to 11 years old and 17.6% of children 12 to 19 years old are obese or morbidly obese in the United States.
It should be noted that, although a few towns with higher population qualify as urban areas, most of the 55 counties within the state of West Virginia have been characterized as rural locations by multiple sources (complete information on rural definitions and distribution of rural areas in West Virginia is provided in the online supplement).
A number of pathophysiologic mechanisms could explain the direct association found in our study between childhood asthma and high serum triglycerides. In particular, this association is consistent with the hypothesis that both pathologic entities result from the changing diet patterns of today's children, regardless of their body mass. For example, children are consuming smaller amounts of antioxidants, which have been shown to protect against asthma (30–32), and have increased their dietary fat intake, which contributes to high serum triglyceride levels (33, 34).
Inflammation is another mechanism by which metabolic abnormalities may promote the development of asthma. Multiple cytokines (e.g., tumor necrosis factor-α, IL-1, IL-6), hormones (e.g., leptin, adiponectin), and acute-phase reactants (e.g., C-reactive protein) participate to both metabolic and inflammatory pathways and are overexpressed in both asthma and obesity resulting in a chronic, low-grade inflammatory state that also promotes insulin resistance (33, 34). However, the limited number of studies that investigated the link between obesity and airway inflammation in children by exhaled nitric oxide (35, 36) or breath condensate (37) were unable to find any association.
Potential limitations of this study include the reliability of parents' reports of physician-diagnosed asthma and mandate the use of objective measurements of pulmonary function in future studies. However, spirometry is logistically impossible in the context of a community-based screening program using a sample of this size, and therefore these follow-up studies will necessarily involve much smaller cohorts. Also, most children diagnosed with asthma have normal pulmonary function at baseline, and their diagnosis is based solely on historical and clinical data. Another potential issue is the predominantly rural nature of the sample, which may be affected by environmental exposures less usual in urban areas (e.g., coal and wood stoves) and may play a part in the higher prevalence of asthma compared with the national average (38). On the other hand, our findings are surprising considering the predominant exposure of our sample to a farming environment, and argue strongly against the basic principles of the “hygiene hypothesis,” or at least suggest that nutritional and metabolic factors can override the protective effect of farming.
Increasing rates of pediatric asthma could also result directly from peripheral tissue insulin resistance and compensatory hyperinsulinemia, which may interfere with the antiinflammatory effects of insulin while increasing bronchial reactivity through inhibition of presynaptic M2 muscarinic receptors (39). In addition, intracellular serine/threonine kinases, such as c-Jun NH(2)-terminal protein kinases, are activated by Toll-like receptor signaling in the context of innate immunoinflammatory responses, and also inhibit insulin signaling (38). Because of the close interdependence between inflammatory and metabolic pathways emerging from this study, pharmacologic ligands of the peroxisome proliferator-activated family of nuclear receptor proteins widely used to treat hyperlipidemia and diabetes may also improve the airway inflammation and hyperreactivity characteristic of asthma.
Of course, our data cannot elucidate the chronologic sequence of events linking hypertriglyceridemia, insulin resistance, and the inflammation seen in obesity and asthma. For example, it is impossible to rule out that airway inflammation is the primary event leading to secondary hypertriglyceridemia and insulin resistance. Therefore, additional studies are needed to explore the complex interactions among the multiple pathways involved in this association and creating increased vulnerability among children. Also, individual differences in pubertal stage may have an influence on some of the study variables, such as insulin resistance, and have to be reassessed in future studies.
Finally, potential error may derive from using AN as a proxy for insulin resistance because, although AN is highly predictive of insulin resistance in adults, its significance in children is less documented and some studies have questioned its predictive value (40, 41). However, a recent analysis of CARDIAC data (42) has shown that 60.8% of children with AN have an abnormal (i.e., ≥3) homeostatic model assessment index of insulin resistance and β-cell function based on fasting plasma glucose and insulin concentrations (43), and other previous studies have shown that children with AN are 1.6 to 4.2 times as likely as their counterparts without AN to have hyperinsulinemia (44, 45). Thus, although this biomarker is less than optimal, it is without doubt associated with insulin resistance and the highly significant relationship between asthma and AN among this large and diverse sample of children warrants further studies to better understand the pathophysiologic link between insulin resistance and asthma in children, which has been confirmed very recently in another small study based on homeostatic model assessment (46).
In conclusion, this study is the first analysis of the relationship between asthma and body mass in a large and diverse sample of school-age children across the entire range of weight percentile categories, and shows that children with physician-diagnosed asthma tend to have higher serum triglyceride levels and higher rates of insulin resistance as predicted by the AN biomarker, regardless of the children's body mass. Thus, dyslipidemia and hyperinsulinemia, known silent precursors to cardiovascular disease and diabetes, may also be associated with the development of asthma and confound its epidemiologic link to obesity. Our findings imply a strong and direct influence of metabolic pathways on the immune mechanisms, both innate and adaptive, involved in the pathogenesis of asthma in children, and suggest that strict monitoring and dietary and pharmacologic control of triglyceride and glucose levels starting in the first years of life may have a critical role in the management of chronic asthma in children.
The authors are grateful for the collaboration and screening expertise of Ms. Violet Pastorial, Georgianna Tillis, Tammy Pyle, and all the WVRHEP Coordinators. They also thank the other CARDIAC team members for their work on the study.
|1.||Global surveillance, prevention and control of chronic respiratory diseases: a comprehensive approach. Geneva: World Health Organization; 2007.|
|2.||The International Study of Asthma and Allergies in Childhood (ISAAC) Steering Committee. Worldwide variation in prevalence of symptoms of asthma, allergic rhinoconjunctivitis, and atopic eczema: ISAAC. Lancet 1998;351:1225–1232.|
|3.||Wojcicki JM, Heyman MB. Let's move: childhood obesity prevention from pregnancy and infancy onward. N Engl J Med 2010;362:1457–1459.|
|4.||Seidell JC, de Grott LC, van Sonsbeek JL, Deurenberg P, Hautvast AJ. Associations of moderate and severe overweight with self-reported illness and medical care in Dutch adults. Am J Public Health 1986;76:264–269.|
|5.||Neri E, Pagano R, Decarli A, La Vecchia C. Body weight and the prevalence of chronic disease. J Epidemiol Community Health 1988;42:24–29.|
|6.||Asher MI, Keil U, Anderson HR, Beasley R, Crane J, Martinez F, Mitchell EA, Pearce N, Sibbald B, Stewart AW, et al. International Study of Asthma and Allergies in Childhood (ISAAC): rationale and methods. Eur Respir J 1995;8:483–491.|
|7.||Beuther DA, Weiss ST, Sutherland ER. Obesity and asthma. Am J Respir Crit Care Med 2006;174:112–119.|
|8.||Chinn S. Obesity and asthma: evidence for and against a causal relation. J Asthma 2003;40:1–16.|
|9.||Rodriguez MA, Winkleby MA, Ahn D, Sundquist J, Kraemer HC. Identification of population subgroups of children and adolescents with high asthma prevalence: findings from the Third National Health and Nutrition Examination Survey. Arch Pediatr Adolesc Med 2002;156:269–275.|
|10.||Weiss ST, Shore S. Obesity and asthma: directions for research. Am J Respir Crit Care Med 2004;169:963–968.|
|11.||Shore SA. Obesity and asthma: possible mechanisms. J Allergy Clin Immunol 2008;121:1087–1093.|
|12.||Cottrell LA, Ice C, Neal WA, Piedimonte G. Metabolic abnormalities in children with asthma: a population-based study. Am J Respir Crit Care Med 2009;179:A3971.|
|13.||Cottrell LA, Neal WA, Piedimonte G. Exploring metabolic abnormalities based on asthma severity and management. Am J Respir Crit Care Med 2010;181:A1876.|
|14.||Demerath E, Muratova V, Spangler E, Li J, Minor VE, Neal WA. School-based obesity screening in rural Appalachia. Prev Med 2003;37:553–560.|
|15.||Neal WA, Demerath E, Gonzales E, Spangler E, Minor VE, Stollings R, Islam S. Coronary Artery Risk Detection In Appalachian Communities (CARDIAC): preliminary findings. W V Med J 2001;97:102–105.|
|16.||Hammer LD, Kraemer HC, Wilson DM, Ritter PL, Dornbusch SM. Standardized percentile curves of body-mass index for children and adolescents. Am J Dis Child 1991;145:259–263.|
|17.||Hud JA Jr, Cohen JB, Wagner JM, Cruz PD Jr. Prevalence and significance of acanthosis nigricans in an adult obese population. Arch Dermatol 1992;128:941–944.|
|18.||Burke JP, Hale DE, Hazuda HP, Stern MP. A quantitative scale of acanthosis nigricans. Diabetes Care 1999;22:1655–1659.|
|19.||Harris CV, Neal WA. Assessing BMI in West Virginia schools: parent perspectives and the influence of context. Pediatrics 2009;124:S63–S72.|
|20.||Neal WA. Disorders of lipoprotein metabolism and transport. In: Kliegman RM, Behrman RE, Jenson HB, Stanton BF, editors. Nelson textbook of pediatrics, 18th ed. Philadelphia: Saunders-Elsevier; 2007. pp. 580–593.|
|21.||Eijkemans M, Mommers M, de Vries SI, van Buuren S, Stafleu A, Bakker I, Thijs C. Asthmatic symptoms, physical activity, and overweight in young children: a cohort study. Pediatrics 2008;121:e666–e672.|
|22.||Goran MI, Ball GD, Cruz ML. Obesity and risk of type 2 diabetes and cardiovascular disease in children and adolescents. J Clin Endocrinol Metab 2003;88:1417–1427.|
|23.||Engstrom G, Hedblad B, Nilsson P, Wollmer P, Berglund G, Janzon L. Lung function, insulin resistance and incidence of cardiovascular disease: a longitudinal cohort study. J Intern Med 2003;253:574–581.|
|24.||Lawlor DA, Ebrahim S, Smith GD. Associations of measures of lung function with insulin resistance and type 2 diabetes: findings from the British Women's Heart and Health Study. Diabetologia 2004;47:195–203.|
|25.||McKeever TM, Weston PJ, Hubbard R, Fogarty A. Lung function and glucose metabolism: an analysis of data from the Third National Health and Nutrition Examination Survey. Am J Epidemiol 2005;161:546–556.|
|26.||Lazarus R, Sparrow D, Weiss ST. Impaired ventilatory function and elevated insulin levels in nondiabetic males: the normative aging study. Eur Respir J 1998;12:635–640.|
|27.||Scuri M, Samsell L, Walton C, Piedimonte G. Maternal high fat diet causes inflammation and airway hyperresponsiveness in rodent offspring. Am J Respir Crit Care Med 2010;181:A1899.|
|28.||Ogden CL, Carroll MD, Flegal KM. High body mass index for age among US children and adolescents, 2003–2006. JAMA 2008;299:2401–2405.|
|29.||Hickman TB, Briefel RR, Carroll MD, Rifkind BM, Cleeman JI, Maurer KR, Johnson CL. Distributions and trends of serum lipid levels among United States children and adolescents ages 4–19 years: data from the Third National Health and Nutrition Examination Survey. Prev Med 1998;27:879–890.|
|30.||Bessesen DH. The role of carbohydrates in insulin resistance. J Nutr 2001;131:2782S–2786S.|
|31.||Devereux G, Seaton A. Diet as a risk factor for atopy and asthma. J Allergy Clin Immunol 2005;115:1109–1117, quiz 1118.|
|32.||Rubin RN, Navon L, Cassano PA. Relationship of serum antioxidants to asthma prevalence in youth. Am J Respir Crit Care Med 2004;169:393–398.|
|33.||Hotamisligil GS, Shargill NS, Spiegelman BM. Adipose expression of tumor necrosis factor-alpha: direct role in obesity-linked insulin resistance. Science 1993;259:87–91.|
|34.||Perseghin G, Petersen K, Shulman GI. Cellular mechanism of insulin resistance: potential links with inflammation. Int J Obes Relat Metab Disord 2003;27:S6–S11.|
|35.||Kattan M, Kumar R, Bloomberg GR, Mitchell HE, Calatroni A, Gergen PJ, Kercsmar CM, Visness CM, Matsui EC, Steinbach SF, et al. Asthma control, adiposity, and adipokines among inner-city adolescents. J Allergy Clin Immunol 2010;125:584–592.|
|36.||Verhulst SL, Aerts L, Jacobs S, Schrauwen N, Haentjens D, Claes R, Vaerenberg H, Van Gaal LF, De Backer WA, Desager KN. Sleep-disordered breathing, obesity, and airway inflammation in children and adolescents. Chest 2008;134:1169–1175.|
|37.||Leung TF, Li CY, Lam CW, Au CS, Yung E, Chan IH, Wong GW, Fok TF. The relation between obesity and asthmatic airway inflammation. Pediatr Allergy Immunol 2004;15:344–350.|
|38.||Wellen KE, Hotamisligil GS. Inflammation, stress, and diabetes. J Clin Invest 2005;115:1111–1119.|
|39.||Al-Shawwa BA, Al-Huniti NH, DeMattia L, Gershan W. Asthma and insulin resistance in morbidly obese children and adolescents. J Asthma 2007;44:469–473.|
|40.||Hirschler V, Aranda C, Oneto A, Gonzalez C, Jadzinsky M. Is acanthosis nigricans a marker of insulin resistance in obese children? Diabetes Care 2002;25:2353.|
|41.||Nguyen TT, Keil MF, Russell DL, Pathomvanich A, Uwaifo GI, Sebring NG, Reynolds JC, Yanovski JA. Relation of acanthosis nigricans to hyperinsulinemia and insulin sensitivity in overweight African American and white children. J Pediatr 2001;138:474–480.|
|42.||Ice CL, Murphy E, Minor VE, Neal W. Metabolic syndrome in 5th grade children with acanthosis nigricans: results from the Coronary Artery Risk Detection In Appalachian Communities project. World J Pediatr 2009;5:23–30.|
|43.||Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and B-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28:412–419.|
|44.||Mukhtar Q, Cleverley G, Voorhees RE, McGrath JW. Prevalence of acanthosis nigricans and its association with hyperinsulinemia in New Mexico adolescents. J Adolesc Health 2001;28:372–376.|
|45.||Stoddart ML, Blevins KS, Lee ET, Wang W, Blackett PR. Association of acanthosis nigricans with hyperinsulinemia compared with other selected risk factors for type 2 diabetes in Cherokee Indians: the Cherokee Diabetes Study. Diabetes Care 2002;25:1009–1014.|
|46.||Arshi M, Cardinal J, Hill RJ, Davies PS, Wainwright C. Asthma and insulin resistance in children. Respirology 2010;15:779–784.|