Insulin resistance, metabolic syndrome, and lung function in US adolescents with and without asthma n, MD, DrPHa Erick Forno, MD, MPH,a* Yueh-Ying Han, PhD,a* Radhika H. Muzumdar, MD,b and Juan C. Celedo Pittsburgh, Pa Background: Obesity increases both the risk of asthma and asthma severity and is a well-known risk factor for insulin resistance and the metabolic syndrome (MS) in children and adolescents. Objective: We aimed to examine the association among obesity, insulin sensitivity, MS, and lung function in US adolescents with and without asthma. Methods: We performed a cross-sectional study of 1429 adolescents aged 12 to 17 years in the 2007-2010 National Health and Nutrition Examination Survey. Adjusted regression was used to assess the relationships among obesity, insulin sensitivity/resistance, MS, and lung function in children with and without asthma. Results: Insulin resistance was negatively associated with FEV1 and forced vital capacity (FVC) in adolescents with and without asthma, whereas MS was associated with lower FEV1/FVC ratios, with a more pronounced decrease found among asthmatic patients; these associations were driven by overweight/obese adolescents. Higher body mass index was associated with a decrease in FEV1/FVC ratios among adolescents with insulin resistance. Compared with healthy participants, adolescents with MS had an approximately 2% decrease in FEV1/FVC ratios, adolescents with asthma had an approximately 6% decrease, and those with MS and asthma had approximately 10% decreased FEV1/FVC ratios (P < .05). Conclusion: Insulin resistance and MS are associated with worsened lung function in overweight/obese adolescents. Asthma and MS synergistically decrease lung function, as do obesity and insulin resistance. These factors might contribute to the pathogenesis of asthma severity in obese patients and warrant further investigation. (J Allergy Clin Immunol 2015;nnn:nnn-nnn.)
From athe Division of Pediatric Pulmonary Medicine, Allergy, and Immunology and bthe Division of Pediatric Endocrinology, Children’s Hospital of Pittsburgh of UPMC, University of Pittsburgh School of Medicine. *These authors contributed equally to this work. E.F.’s contribution was supported by National Institute of Health (NIH) grant HD052892. J.C.C.’s contribution was supported by NIH grants HL079966 and HL117191 and by an endowment from the Heinz Foundation. Disclosure of potential conflict of interest: J. C. Celedon’s contribution was supported by grants HL079966 and HL117191 from the National Institutes of Health (NIH) and by an endowment from the Heinz Foundation. E. Forno’s contribution was supported by grant HD052892 from the NIH. The rest of the authors declare that they have no relevant conflicts of interest. Received for publication October 15, 2014; revised January 13, 2015; accepted for publication January 21, 2015. Corresponding author: Erick Forno, MD, MPH, Division of Pulmonary Medicine, Allergy and Immunology, Children’s Hospital of Pittsburgh of UPMC, 4401 Penn Ave, Pittsburgh, PA 15224. E-mail: [email protected]. 0091-6749/$36.00 Ó 2015 American Academy of Allergy, Asthma & Immunology http://dx.doi.org/10.1016/j.jaci.2015.01.010
Key words: Asthma, lung function, insulin resistance, adiposity, obesity, metabolic syndrome, National Health and Nutrition Examination Survey
Asthma and obesity are major public health issues in industrialized countries, such as the United States, with parallel increases in the prevalence of both diseases over the last few decades.1-4 Epidemiologic studies have shown that childhood obesity is associated with increased risk of incident asthma, increased asthma severity and morbidity, and decreased response to long-term asthma medications.5-8 Childhood obesity is a known risk factor for insulin resistance, diabetes, and the metabolic syndrome (MS).9,10 There is growing evidence that metabolic derangements, such as hyperglycemia and hyperinsulinemia, can lead to airway dysfunction and increased airway responsiveness through several pathways, including epithelial damage and airway smooth muscle proliferation.11 A recent population-based study reported higher rates of acanthosis nigricans (a marker of insulin resistance) in children with asthma than in those without asthma, regardless of body mass index (BMI).12 Conversely, morbidly obese children and adolescents with asthma have a higher incidence of insulin resistance than morbidly obese children and adolescents without asthma.13,14 The MS has also been significantly associated with lung function impairment and asthma-like symptoms, with abdominal obesity being the key determinant of this association.15,16 We hypothesized that measures of insulin sensitivity (fasting glucose/insulin [G/I] ratio and the quantitative insulin sensitivity check index [QUICKI]) and insulin resistance (homeostasis model assessment-estimated insulin resistance [HOMA-IR]) are associated with lung function in adolescents, particularly among those with obesity or increased adiposity. We further hypothesized that the MS is associated with worse lung function and that detrimental effects of insulin resistance and MS on lung function are more pronounced in adolescents with asthma. We examined these hypotheses in a cross-sectional study of adolescents living in the United States.
METHODS Subject recruitment The National Health and Nutrition Examination Survey (NHANES) is a cross-sectional nationwide survey designed to assess the health and nutritional status of the noninstitutionalized population of the United States.17 NHANES combines interviews and physical examinations of participants by highly trained personnel. Participants for the study are selected by using a stratified multistage probability design and are thus a representative sample of the US population. By design, ethnic minorities (African Americans and Mexican Americans) are oversampled to increase the statistical power for data analysis in these groups. Adolescents 12 to 17 years of age who participated in the 2007-2008 and 2009-2010 NHANESs were included in this analysis. Current 1
2 FORNO ET AL
Abbreviations used BMI: Body mass index CRP: C-reactive protein FVC: Forced vital capacity G/I: Glucose/insulin HDL: High-density lipoprotein HOMA-IR: Homeostasis model assessment-estimated insulin resistance MS: Metabolic syndrome NHANES: National Health and Nutrition Examination Survey PBF: Percentage body fat QUICKI: Quantitative insulin sensitivity check index WC: Waist circumference WHtR: Waist/height ratio
asthma was defined as both having had asthma diagnosed by a doctor or other health care professional and at least 1 asthma attack in the past year. Participants who had neither diagnosed asthma nor an asthma attack in the past year were selected as control subjects. Participants who reported a lifetime diagnosis of asthma but no asthma attacks in the past year were excluded from this analysis. NHANES was approved by the Institutional Review Board of the National Center for Health Statistics of the US Centers for Disease Control and Prevention. Informed consent was obtained from all participants.
Study procedures Measures of obesity and adiposity were collected by trained health technicians, according to recommendations from the Anthropometric standardization reference manual.18 BMI was calculated as weight (in kilograms) divided by height (in meters squared). Percentage body fat (PBF) was calculated from tricipital and subscapular skin folds. For data analysis, all measures were transformed to z scores to obtain standardized and comparable coefficients: BMI z scores were calculated by using equations based on the 2000 US Centers for Disease Control and Prevention growth charts,19 PBF z scores were calculated by using reference equations for US children,20 and waist circumference (WC) and waist/height ratio (WHtR) values were standardized by using the distribution of these measures in our study population. Overweight/obese was defined as a z score of greater than 1.0364 (85th percentile) for each adiposity indicator. Fasting plasma glucose, serum insulin, high-density lipoprotein (HDL) cholesterol, triglyceride, and C-reactive protein (CRP) levels were measured at a morning examination session in all NHANES participants aged 12 years and older. Participants fasting for less than 9 hours, taking insulin or oral medications for diabetes, or refusing phlebotomy were excluded. Insulin sensitivity was measured by using 2 indicators: fasting glucose (in microunits per milliliter)/insulin (in milligrams per deciliter [G/I]) ratio and QUICKI value. The QUICKI value was defined as follows: 1=log½Fasting insulin1log½Fasting glucose:21 Conversely, the HOMA-IR value was used as a measure of insulin resistance and was defined as follows: ðFasting insulin 3 Fasting glucose½mmol=LÞ=22:5:22 Systolic blood pressure was measured in all NHANES participants according to study protocols23 and was standardized for this analysis by using its distribution in our study population. MS was defined as meeting at least 3 of the following 5 criteria: fasting glucose level of 110 mg/dL or greater, WC value of the 75th percentile or greater, fasting triglyceride level of 100 mg/ dL or greater, HDL level of 50 mg/dL or less, and systolic blood pressure of the 90th percentile or greater.14,24 Spirometry was performed according to American Thoracic Society recommendations.25 The best FEV1 and forced vital capacity (FVC) values were selected for data analysis. Participants were not eligible for spirometry
J ALLERGY CLIN IMMUNOL nnn 2015
if they were receiving supplemental oxygen or had painful ear infections, current chest pain or a physical problem with forceful expiration, recent surgery (of the eye, chest, or abdomen), heart disease, or tuberculosis. Our main analyses were performed by using absolute values (in milliliters) adjusted for age, sex, height, and height squared; confirmatory analyses were performed with NHANES III predictive equations for lung function measures and are included in this article’s Online Repository at www.jacionline.org.
Statistical analysis Primary sampling units and strata for the complex design of NHANES were taken into account for data analysis. Sampling weights, stratification, and clusters provided in the NHANES data set were incorporated into the analysis to obtain proper estimates and SEs; fasting sample weights were also used when analyzing fasting glucose and insulin levels. All multivariate analyses were performed with linear regression within the SURVEY procedure in SAS software (SAS Institute, Cary, NC). All models were adjusted for age, sex, race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, or other), family history of asthma, health insurance coverage, environmental tobacco smoke exposure, CRP level, and number of fasting hours; models for FEV1 and FVC were additionally adjusted for height and height squared. In a secondary multivariate analysis we examined the relation between FEV1/FVC ra_85th percentile for age and sex) after tios and overweight/obesity (BMI > stratification by asthma and MS. All statistical analyses were conducted with SAS 9.3 software.
RESULTS The main characteristics of the 1429 participating adolescents with and without current asthma are shown in Table I (see Fig E1 in this article’s Online Repository at www.jacionline.org for derivation of the study sample from NHANES). Compared with adolescents without asthma, those with asthma were more likely to be black and to have a low annual household income, health insurance, a family history of asthma, lower FEV1, and lower FEV1/ FVC ratios (P < .05 in all instances). Adolescents with asthma also had a nonsignificant trend toward higher adiposity z scores than their nonasthmatic counterparts (P < .10). There were no statistically significant differences in age, sex, environmental tobacco smoke exposure, CRP levels, or indicators of MS or insulin resistance between adolescents with and without asthma. Similar results were found when comparing adolescents with ‘‘ever asthma’’ with healthy control subjects (see Table E1 in this article’s Online Repository at www.jacionline.org). Table II shows the results of the multivariate analysis of the relation between indicators of insulin sensitivity or resistance and lung function measures among all subjects and after stratification by asthma status. In this analysis greater insulin sensitivity (defined by higher G/I ratios or QUICKI values) was significantly associated with higher FEV1 and FVC values in all subjects. Among subjects without asthma, each unit increment in G/I ratio was significantly associated with approximately 11- to 13-mL increments in FEV1 and FVC values, respectively, and each 0.01point increment in QUICKI value was significantly associated with approximately 24- to 30-mL increments in FEV1 and FVC values, respectively. Among adolescents with asthma, these findings were more pronounced: significant increments of approximately 20 mL in FEV1 and approximately 45 mL in FVC per each unit increment in G/I ratio and significant increments of approximately 42 mL in FEV1 and approximately 59 mL in FVC per each unit increment in QUICKI value. Conversely, insulin resistance (defined by higher HOMA-IR values) was associated with lower FEV1 and FVC values: each unit increase in HOMA-IR
FORNO ET AL 3
J ALLERGY CLIN IMMUNOL VOLUME nnn, NUMBER nn
TABLE I. Characteristics of study participants by asthma status Characteristics
No asthma (n 5 1334)
Age (y) 14.54 6 0.05 Male sex 688 (50.03) Race/ethnicity Non-Hispanic white 439 (60.51) Non-Hispanic black 294 (13.34) Hispanic 530 (19.55) Other 71 (6.61) Household income percentile
Fasting glucose _110 mg/dL >
1.66) 21.65 (23.37 to 0.08) 21.68 (26.26 to 2.90) 1.80) 21.94 (23.79 to 20.10)à 21.70 (26.33 to 2.93) 1.80) 2.82 (1.06 to 4.57)§ NA 2.44) NA 22.85 (28.79 to 3.08) 1.69) 21.54 (23.21 to 0.23) 2.07 (21.32 to 5.45)
Data are presented as b coefficients (95% CIs). Values in boldface indicate statistical significance. All models were adjusted for age, sex, race/ethnicity, health insurance coverage, family history of asthma, environmental tobacco smoke exposure, fasting hours, and CRP levels. SBP, Systolic blood pressure. *Defined on the basis of BMI z scores (additionally adjusted for current asthma). All asthmatic patients had fasting glucose levels of less than 110 mg/dL, and all children of normal weight had WC values of less than the 75th percentile. àP < .05. §P < .01.
J ALLERGY CLIN IMMUNOL VOLUME nnn, NUMBER nn
pathogenesis of asthma severity in obese patients and warrant further investigation. Clinical implications: Insulin resistance and the MS might affect lung function in adolescents. Management of these conditions should be part of the treatment of obese asthmatic children and adolescents. REFERENCES 1. Akinbami LJ, Moorman JE, Bailey C, Zahran HS, King M, Johnson CA, et al. Trends in asthma prevalence, health care use, and mortality in the united states, 2001-2010. NCHS Data Brief 2012;(94):1-8. 2. Akinbami LJ, Moorman JE, Garbe PL, Sondik EJ. Status of childhood asthma in the united states, 1980-2007. Pediatrics 2009;123(suppl 3):S131-45. 3. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of obesity among adults: United states, 2011-2012. NCHS Data Brief 2013;(131):1-8. 4. Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal KM. Prevalence of overweight and obesity in the united states, 1999-2004. JAMA 2006; 295:1549-55. 5. Dixon AE, Holguin F, Sood A, Salome CM, Pratley RE, Beuther DA, et al. An official American Thoracic Society workshop report: obesity and asthma. Proc Am Thorac Soc 2010;7:325-35. 6. Forno E, Acosta-Perez E, Brehm J, Han YY, Alvarez M, Colon-Semidey A, et al. Obesity and adiposity indicators, asthma, and atopy in Puerto Rican children. J Allergy Clin Immunol 2014;133:1308-14, e1-5. 7. Forno E, Lescher R, Strunk R, Weiss S, Fuhlbrigge A, Celedon JC, et al. Decreased response to inhaled steroids in overweight and obese asthmatic children. J Allergy Clin Immunol 2011;127:741-9. 8. Borrell LN, Nguyen EA, Roth LA, Oh SS, Tcheurekdjian H, Sen S, et al. Childhood obesity and asthma control in the GALA II and SAGE II studies. Am J Respir Crit Care Med 2013;187:697-702. 9. Lee JM, Okumura MJ, Davis MM, Herman WH, Gurney JG. Prevalence and determinants of insulin resistance among U.S. adolescents: a population-based study. Diabetes Care 2006;29:2427-32. 10. Weiss R, Dziura J, Burgert TS, Tamborlane WV, Taksali SE, Yeckel CW, et al. Obesity and the metabolic syndrome in children and adolescents. N Engl J Med 2004;350:2362-74. 11. Agrawal A, Mabalirajan U, Ahmad T, Ghosh B. Emerging interface between metabolic syndrome and asthma. Am J Respir Cell Mol Biol 2011;44:270-5. 12. Cottrell L, Neal WA, Ice C, Perez MK, Piedimonte G. Metabolic abnormalities in children with asthma. Am J Respir Crit Care Med 2011;183:441-8. 13. Al-Shawwa BA, Al-Huniti NH, DeMattia L, Gershan W. Asthma and insulin resistance in morbidly obese children and adolescents. J Asthma 2007;44:469-73. 14. Del-Rio-Navarro BE, Castro-Rodriguez JA, Garibay Nieto N, Berber A, Toussaint G, Sienra-Monge JJ, et al. Higher metabolic syndrome in obese asthmatic compared to obese nonasthmatic adolescent males. J Asthma 2010;47:501-6. 15. Leone N, Courbon D, Thomas F, Bean K, Jego B, Leynaert B, et al. Lung function impairment and metabolic syndrome: the critical role of abdominal obesity. Am J Respir Crit Care Med 2009;179:509-16. 16. Lee EJ, In KH, Ha ES, Lee KJ, Hur GY, Kang EH, et al. Asthma-like symptoms are increased in the metabolic syndrome. J Asthma 2009;46:339-42. 17. Centers for Disease Control and Prevention. 2007-2008 national health and nutrition examination survey (NHANES) survey operations manuals, brochures, consent documents. 2012. Available at: http://www.cdc.gov/nchs/nhanes/nhanes2007-2008/ current_nhanes_07_08.htm. Accessed February 18, 2014. 18. Lohman TG, Roche AF. Anthropometric standardization reference manual. Champaign (IL): Human Kinetics Books; 1988. 19. Centers for Disease Control and Prevention. A SAS program for the CDC growth charts. 2011. Available at: http://www.cdc.gov/nccdphp/dnpao/growthcharts/resources/sas. htm. Accessed February 18, 2014. 20. Slaughter MH, Lohman TG, Boileau RA, Horswill CA, Stillman RJ, Van Loan MD, et al. Skinfold equations for estimation of body fatness in children and youth. Hum Biol 1988;60:709-23. 21. Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, et al. Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab 2000;85:2402-10. 22. Wallace TM, Levy JC, Matthews DR. Use and abuse of HOMA modeling. Diabetes Care 2004;27:1487-95. 23. Ostchega Y, Prineas RJ, Paulose-Ram R, Grim CM, Willard G, Collins D. National health and nutrition examination survey 1999-2000: effect of observer training and protocol standardization on reducing blood pressure measurement error. J Clin Epidemiol 2003;56:768-74.
FORNO ET AL 7
24. de Ferranti SD, Gauvreau K, Ludwig DS, Neufeld EJ, Newburger JW, Rifai N. Prevalence of the metabolic syndrome in American adolescents: findings from the third national health and nutrition examination survey. Circulation 2004; 110:2494-7. 25. Miller MR, Hankinson J, Brusasco V, Burgos F, Casaburi R, Coates A, et al. Standardisation of spirometry. Eur Respir J 2005;26:319-38. 26. Lecube A, Sampol G, Munoz X, Lloberes P, Hernandez C, Simo R. Insulin resistance is related to impaired lung function in morbidly obese women: a casecontrol study. Diabetes Metab Res Rev 2010;26:639-45. 27. Lim SY, Rhee EJ, Sung KC. Metabolic syndrome, insulin resistance and systemic inflammation as risk factors for reduced lung function in Korean nonsmoking males. J Korean Med Sci 2010;25:1480-6. 28. Tantisira KG, Litonjua AA, Weiss ST, Fuhlbrigge AL. Childhood Asthma Management Program Research Group. Association of body mass with pulmonary function in the childhood asthma management program (CAMP). Thorax 2003; 58:1036-41. 29. Vo P, Makker K, Matta-Arroyo E, Hall CB, Arens R, Rastogi D. The association of overweight and obesity with spirometric values in minority children referred for asthma evaluation. J Asthma 2013;50:56-63. 30. Noveral JP, Bhala A, Hintz RL, Grunstein MM, Cohen P. Insulin-like growth factor axis in airway smooth muscle cells. Am J Physiol Lung Cell Mol Physiol 1994;267:L761-5. 31. Dekkers BG, Schaafsma D, Tran T, Zaagsma J, Meurs H. Insulin-induced laminin expression promotes a hypercontractile airway smooth muscle phenotype. Am J Respir Cell Mol Biol 2009;41:494-504. 32. Cohen P, Noveral JP, Bhala A, Nunn SE, Herrick DJ, Grunstein MM. Leukotriene d4 facilitates airway smooth muscle cell proliferation via modulation of the IGF axis. Am J Physiol Lung Cell Mol Physiol 1995;269:L151-7. 33. Sood A, Shore SA. Adiponectin, leptin, and resistin in asthma: basic mechanisms through population studies. J Allergy (Cairo) 2013;2013:785835. 34. Kattan M, Kumar R, Bloomberg GR, Mitchell HE, Calatroni A, Gergen PJ, et al. Asthma control, adiposity, and adipokines among inner-city adolescents. J Allergy Clin Immunol 2010;125:584-92. 35. Nagel G, Koenig W, Rapp K, Wabitsch M, Zoellner I, Weiland SK. Associations of adipokines with asthma, rhinoconjunctivitis, and eczema in German schoolchildren. Pediatr Allergy Immunol 2009;20:80-1. 36. Mark AL. Selective leptin resistance revisited. Am J Physiol Regul Integr Comp Physiol 2013;305:R566-81. 37. Nasrallah MP, Ziyadeh FN. Overview of the physiology and pathophysiology of leptin with special emphasis on its role in the kidney. Semin Nephrol 2013;33: 54-65. 38. Jiang S, Park DW, Tadie JM, Gregoire M, Deshane J, Pittet JF, et al. Human resistin promotes neutrophil proinflammatory activation and neutrophil extracellular trap formation and increases severity of acute lung injury. J Immunol 2014;192:4795-803. 39. Mishra A, Wang M, Schlotman J, Nikolaidis NM, DeBrosse CW, Karow ML, et al. Resistin-like molecule-beta is an allergen-induced cytokine with inflammatory and remodeling activity in the murine lung. Am J Physiol Lung Cell Mol Physiol 2007;293:L305-13. 40. Kim KM, Kim SS, Lee SH, Song WJ, Chang YS, Min KU, et al. Association of insulin resistance with bronchial hyperreactivity. Asia Pac Allergy 2014;4: 99-105. 41. Sarlus H, Wang X, Cedazo-Minguez A, Schultzberg M, Oprica M. Chronic airway-induced allergy in mice modifies gene expression in the brain toward insulin resistance and inflammatory responses. J Neuroinflam 2013;10:99. 42. Brumpton BM, Camargo CA Jr, Romundstad PR, Langhammer A, Chen Y, Mai XM. Metabolic syndrome and incidence of asthma in adults: the HUNT study. Eur Respir J 2013;42:1495-502. 43. Fimognari FL, Pasqualetti P, Moro L, Franco A, Piccirillo G, Pastorelli R, et al. The association between metabolic syndrome and restrictive ventilatory dysfunction in older persons. J Gerontology A Biol Sci Med Sci 2007;62:760-5. 44. Nakajima K, Kubouchi Y, Muneyuki T, Ebata M, Eguchi S, Munakata H. A possible association between suspected restrictive pattern as assessed by ordinary pulmonary function test and the metabolic syndrome. Chest 2008;134: 712-8. 45. van Huisstede A, Cabezas MC, Birnie E, van de Geijn GJ, Rudolphus A, Mannaerts G, et al. Systemic inflammation and lung function impairment in morbidly obese subjects with the metabolic syndrome. J Obes 2013;2013:131349. 46. Leishangthem GD, Mabalirajan U, Singh VP, Agrawal A, Ghosh B, Dinda AK. Ultrastructural changes of airway in murine models of allergy and diet-induced metabolic syndrome. ISRN Allergy 2013;2013:261297. 47. Rojas-Dotor S, Segura-Mendez NH, Miyagui-Namikawa K, Mondragon-Gonzalez R. Expression of resistin, CXCR3, IP-10, CCR5 and MIP-1alpha in obese patients with different severity of asthma. Biol Res 2013;46:13-20.
8 FORNO ET AL
48. Deiuliis JA, Oghumu S, Duggineni D, Zhong J, Rutsky J, Banerjee A, et al. Cxcr3 modulates obesity-induced visceral adipose inflammation and systemic insulin resistance. Obesity 2014;22:1264-74. 49. Hsiao FC, Wu CZ, Su SC, Sun MT, Hsieh CH, Hung YJ, et al. Baseline forced expiratory volume in the first second as an independent predictor of development of the metabolic syndrome. Metabolism 2010;59: 848-53.
J ALLERGY CLIN IMMUNOL nnn 2015
50. Ford ES, Li C, Zhao G, Pearson WS, Mokdad AH. Prevalence of the metabolic syndrome among U.S. adolescents using the definition from the International Diabetes Federation. Diabetes Care 2008;31:587-9. 51. Agudelo GM, Bedoya G, Estrada A, Patino FA, Munoz AM, Velasquez CM. Variations in the prevalence of metabolic syndrome in adolescents according to different criteria used for diagnosis: which definition should be chosen for this age group? Metab Syndr Relat Disord 2014;12:202-9.
J ALLERGY CLIN IMMUNOL VOLUME nnn, NUMBER nn
FIG E1. Derivation of the study sample from NHANES 2007-2008 and 20092010.
FORNO ET AL 8.e1
8.e2 FORNO ET AL
J ALLERGY CLIN IMMUNOL nnn 2015
FIG E2. Predicted FEV1 by asthma and MS status. All models were adjusted for age, sex, race/ethnicity, health insurance coverage, family history of asthma, environmental tobacco smoke exposure, fasting hours, CRP levels, and z scores for each adiposity indicator (A, BMI; B, PBF; C, WC; D, WHtR). No asthma and no MS, n 5 496; MS only, n 5 58; asthma only, n 5 23; and MS and asthma, n 5 7. *P < .05 compared with the control group (no asthma and no MS).
FORNO ET AL 8.e3
J ALLERGY CLIN IMMUNOL VOLUME nnn, NUMBER nn
TABLE E1. Characteristics of study participants by ‘‘ever asthma’’ status Characteristics
No asthma (n 5 1334)
Ever asthma (n 5 315)
Age (y) 14.54 6 0.05 14.82 6 0.12 Male sex 688 (50.03) 177 (53.95) Race/ethnicity Non-Hispanic white 439 (60.51) 106 (61.68) Non-Hispanic black 294 (13.34) 90 (16.07) Hispanic 530 (19.55) 98 (14.79) Other 71 (6.61) 21 (7.47) Household income
21.68 (26.26 to 21.70 (26.33 to NA 22.85 (28.79 to 2.07 (21.32 to
2.90) 2.93) 3.08) 5.45)
534.77) 552.71) 944.14) 186.53)
Data are presented as b coefficients (95% CIs). Values in boldface indicate statistical significance. All models were adjusted for age, sex, race/ethnicity, health insurance coverage, family history of asthma, environmental tobacco smoke exposure, fasting hours, and CRP levels. SBP, Systolic blood pressure. *Defined on the basis of BMI z scores (additionally adjusted for current asthma). All asthmatic patients had fasting glucose levels of less than 110 mg/dL, and all children of normal weight had WC values of less than the 75th percentile. àP < .05. §P < .01.
FORNO ET AL 8.e5
8.e6 FORNO ET AL
J ALLERGY CLIN IMMUNOL nnn 2015
TABLE E4. Insulin sensitivity/resistance, MS, and percent predicted FEV1, FVC, and FEV1/FVC ratio by asthma status among white, black, and Mexican American participants Outcome
All Participants (n 5 446) FEV1 (% predicted)à FVC (% predicted)à FEV1/FVC ratio (% predicted)à No asthma (n 5 421) FEV1 (% predicted)à FVC (% predicted)à FEV1/FVC ratio (% predicted)à Current asthma (n 5 25) FEV1 (% predicted)à FVC (% predicted)à FEV1/FVC ratio (% predicted)à
G/I ratio
QUICKI value*
HOMA-IR value
MS
3.11 (0.90 to 5.33)k 2.75 (0.73 to 4.76)k
From athe Division of Pediatric Pulmonary Medicine, Allergy, and Immunology and bthe Division of Pediatric Endocrinology, Children’s Hospital of Pittsburgh of UPMC, University of Pittsburgh School of Medicine. *These authors contributed equally to this work. E.F.’s contribution was supported by National Institute of Health (NIH) grant HD052892. J.C.C.’s contribution was supported by NIH grants HL079966 and HL117191 and by an endowment from the Heinz Foundation. Disclosure of potential conflict of interest: J. C. Celedon’s contribution was supported by grants HL079966 and HL117191 from the National Institutes of Health (NIH) and by an endowment from the Heinz Foundation. E. Forno’s contribution was supported by grant HD052892 from the NIH. The rest of the authors declare that they have no relevant conflicts of interest. Received for publication October 15, 2014; revised January 13, 2015; accepted for publication January 21, 2015. Corresponding author: Erick Forno, MD, MPH, Division of Pulmonary Medicine, Allergy and Immunology, Children’s Hospital of Pittsburgh of UPMC, 4401 Penn Ave, Pittsburgh, PA 15224. E-mail: [email protected]. 0091-6749/$36.00 Ó 2015 American Academy of Allergy, Asthma & Immunology http://dx.doi.org/10.1016/j.jaci.2015.01.010
Key words: Asthma, lung function, insulin resistance, adiposity, obesity, metabolic syndrome, National Health and Nutrition Examination Survey
Asthma and obesity are major public health issues in industrialized countries, such as the United States, with parallel increases in the prevalence of both diseases over the last few decades.1-4 Epidemiologic studies have shown that childhood obesity is associated with increased risk of incident asthma, increased asthma severity and morbidity, and decreased response to long-term asthma medications.5-8 Childhood obesity is a known risk factor for insulin resistance, diabetes, and the metabolic syndrome (MS).9,10 There is growing evidence that metabolic derangements, such as hyperglycemia and hyperinsulinemia, can lead to airway dysfunction and increased airway responsiveness through several pathways, including epithelial damage and airway smooth muscle proliferation.11 A recent population-based study reported higher rates of acanthosis nigricans (a marker of insulin resistance) in children with asthma than in those without asthma, regardless of body mass index (BMI).12 Conversely, morbidly obese children and adolescents with asthma have a higher incidence of insulin resistance than morbidly obese children and adolescents without asthma.13,14 The MS has also been significantly associated with lung function impairment and asthma-like symptoms, with abdominal obesity being the key determinant of this association.15,16 We hypothesized that measures of insulin sensitivity (fasting glucose/insulin [G/I] ratio and the quantitative insulin sensitivity check index [QUICKI]) and insulin resistance (homeostasis model assessment-estimated insulin resistance [HOMA-IR]) are associated with lung function in adolescents, particularly among those with obesity or increased adiposity. We further hypothesized that the MS is associated with worse lung function and that detrimental effects of insulin resistance and MS on lung function are more pronounced in adolescents with asthma. We examined these hypotheses in a cross-sectional study of adolescents living in the United States.
METHODS Subject recruitment The National Health and Nutrition Examination Survey (NHANES) is a cross-sectional nationwide survey designed to assess the health and nutritional status of the noninstitutionalized population of the United States.17 NHANES combines interviews and physical examinations of participants by highly trained personnel. Participants for the study are selected by using a stratified multistage probability design and are thus a representative sample of the US population. By design, ethnic minorities (African Americans and Mexican Americans) are oversampled to increase the statistical power for data analysis in these groups. Adolescents 12 to 17 years of age who participated in the 2007-2008 and 2009-2010 NHANESs were included in this analysis. Current 1
2 FORNO ET AL
Abbreviations used BMI: Body mass index CRP: C-reactive protein FVC: Forced vital capacity G/I: Glucose/insulin HDL: High-density lipoprotein HOMA-IR: Homeostasis model assessment-estimated insulin resistance MS: Metabolic syndrome NHANES: National Health and Nutrition Examination Survey PBF: Percentage body fat QUICKI: Quantitative insulin sensitivity check index WC: Waist circumference WHtR: Waist/height ratio
asthma was defined as both having had asthma diagnosed by a doctor or other health care professional and at least 1 asthma attack in the past year. Participants who had neither diagnosed asthma nor an asthma attack in the past year were selected as control subjects. Participants who reported a lifetime diagnosis of asthma but no asthma attacks in the past year were excluded from this analysis. NHANES was approved by the Institutional Review Board of the National Center for Health Statistics of the US Centers for Disease Control and Prevention. Informed consent was obtained from all participants.
Study procedures Measures of obesity and adiposity were collected by trained health technicians, according to recommendations from the Anthropometric standardization reference manual.18 BMI was calculated as weight (in kilograms) divided by height (in meters squared). Percentage body fat (PBF) was calculated from tricipital and subscapular skin folds. For data analysis, all measures were transformed to z scores to obtain standardized and comparable coefficients: BMI z scores were calculated by using equations based on the 2000 US Centers for Disease Control and Prevention growth charts,19 PBF z scores were calculated by using reference equations for US children,20 and waist circumference (WC) and waist/height ratio (WHtR) values were standardized by using the distribution of these measures in our study population. Overweight/obese was defined as a z score of greater than 1.0364 (85th percentile) for each adiposity indicator. Fasting plasma glucose, serum insulin, high-density lipoprotein (HDL) cholesterol, triglyceride, and C-reactive protein (CRP) levels were measured at a morning examination session in all NHANES participants aged 12 years and older. Participants fasting for less than 9 hours, taking insulin or oral medications for diabetes, or refusing phlebotomy were excluded. Insulin sensitivity was measured by using 2 indicators: fasting glucose (in microunits per milliliter)/insulin (in milligrams per deciliter [G/I]) ratio and QUICKI value. The QUICKI value was defined as follows: 1=log½Fasting insulin1log½Fasting glucose:21 Conversely, the HOMA-IR value was used as a measure of insulin resistance and was defined as follows: ðFasting insulin 3 Fasting glucose½mmol=LÞ=22:5:22 Systolic blood pressure was measured in all NHANES participants according to study protocols23 and was standardized for this analysis by using its distribution in our study population. MS was defined as meeting at least 3 of the following 5 criteria: fasting glucose level of 110 mg/dL or greater, WC value of the 75th percentile or greater, fasting triglyceride level of 100 mg/ dL or greater, HDL level of 50 mg/dL or less, and systolic blood pressure of the 90th percentile or greater.14,24 Spirometry was performed according to American Thoracic Society recommendations.25 The best FEV1 and forced vital capacity (FVC) values were selected for data analysis. Participants were not eligible for spirometry
J ALLERGY CLIN IMMUNOL nnn 2015
if they were receiving supplemental oxygen or had painful ear infections, current chest pain or a physical problem with forceful expiration, recent surgery (of the eye, chest, or abdomen), heart disease, or tuberculosis. Our main analyses were performed by using absolute values (in milliliters) adjusted for age, sex, height, and height squared; confirmatory analyses were performed with NHANES III predictive equations for lung function measures and are included in this article’s Online Repository at www.jacionline.org.
Statistical analysis Primary sampling units and strata for the complex design of NHANES were taken into account for data analysis. Sampling weights, stratification, and clusters provided in the NHANES data set were incorporated into the analysis to obtain proper estimates and SEs; fasting sample weights were also used when analyzing fasting glucose and insulin levels. All multivariate analyses were performed with linear regression within the SURVEY procedure in SAS software (SAS Institute, Cary, NC). All models were adjusted for age, sex, race/ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, or other), family history of asthma, health insurance coverage, environmental tobacco smoke exposure, CRP level, and number of fasting hours; models for FEV1 and FVC were additionally adjusted for height and height squared. In a secondary multivariate analysis we examined the relation between FEV1/FVC ra_85th percentile for age and sex) after tios and overweight/obesity (BMI > stratification by asthma and MS. All statistical analyses were conducted with SAS 9.3 software.
RESULTS The main characteristics of the 1429 participating adolescents with and without current asthma are shown in Table I (see Fig E1 in this article’s Online Repository at www.jacionline.org for derivation of the study sample from NHANES). Compared with adolescents without asthma, those with asthma were more likely to be black and to have a low annual household income, health insurance, a family history of asthma, lower FEV1, and lower FEV1/ FVC ratios (P < .05 in all instances). Adolescents with asthma also had a nonsignificant trend toward higher adiposity z scores than their nonasthmatic counterparts (P < .10). There were no statistically significant differences in age, sex, environmental tobacco smoke exposure, CRP levels, or indicators of MS or insulin resistance between adolescents with and without asthma. Similar results were found when comparing adolescents with ‘‘ever asthma’’ with healthy control subjects (see Table E1 in this article’s Online Repository at www.jacionline.org). Table II shows the results of the multivariate analysis of the relation between indicators of insulin sensitivity or resistance and lung function measures among all subjects and after stratification by asthma status. In this analysis greater insulin sensitivity (defined by higher G/I ratios or QUICKI values) was significantly associated with higher FEV1 and FVC values in all subjects. Among subjects without asthma, each unit increment in G/I ratio was significantly associated with approximately 11- to 13-mL increments in FEV1 and FVC values, respectively, and each 0.01point increment in QUICKI value was significantly associated with approximately 24- to 30-mL increments in FEV1 and FVC values, respectively. Among adolescents with asthma, these findings were more pronounced: significant increments of approximately 20 mL in FEV1 and approximately 45 mL in FVC per each unit increment in G/I ratio and significant increments of approximately 42 mL in FEV1 and approximately 59 mL in FVC per each unit increment in QUICKI value. Conversely, insulin resistance (defined by higher HOMA-IR values) was associated with lower FEV1 and FVC values: each unit increase in HOMA-IR
FORNO ET AL 3
J ALLERGY CLIN IMMUNOL VOLUME nnn, NUMBER nn
TABLE I. Characteristics of study participants by asthma status Characteristics
No asthma (n 5 1334)
Age (y) 14.54 6 0.05 Male sex 688 (50.03) Race/ethnicity Non-Hispanic white 439 (60.51) Non-Hispanic black 294 (13.34) Hispanic 530 (19.55) Other 71 (6.61) Household income percentile
Fasting glucose _110 mg/dL >
1.66) 21.65 (23.37 to 0.08) 21.68 (26.26 to 2.90) 1.80) 21.94 (23.79 to 20.10)à 21.70 (26.33 to 2.93) 1.80) 2.82 (1.06 to 4.57)§ NA 2.44) NA 22.85 (28.79 to 3.08) 1.69) 21.54 (23.21 to 0.23) 2.07 (21.32 to 5.45)
Data are presented as b coefficients (95% CIs). Values in boldface indicate statistical significance. All models were adjusted for age, sex, race/ethnicity, health insurance coverage, family history of asthma, environmental tobacco smoke exposure, fasting hours, and CRP levels. SBP, Systolic blood pressure. *Defined on the basis of BMI z scores (additionally adjusted for current asthma). All asthmatic patients had fasting glucose levels of less than 110 mg/dL, and all children of normal weight had WC values of less than the 75th percentile. àP < .05. §P < .01.
J ALLERGY CLIN IMMUNOL VOLUME nnn, NUMBER nn
pathogenesis of asthma severity in obese patients and warrant further investigation. Clinical implications: Insulin resistance and the MS might affect lung function in adolescents. Management of these conditions should be part of the treatment of obese asthmatic children and adolescents. REFERENCES 1. Akinbami LJ, Moorman JE, Bailey C, Zahran HS, King M, Johnson CA, et al. Trends in asthma prevalence, health care use, and mortality in the united states, 2001-2010. NCHS Data Brief 2012;(94):1-8. 2. Akinbami LJ, Moorman JE, Garbe PL, Sondik EJ. Status of childhood asthma in the united states, 1980-2007. Pediatrics 2009;123(suppl 3):S131-45. 3. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of obesity among adults: United states, 2011-2012. NCHS Data Brief 2013;(131):1-8. 4. Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal KM. Prevalence of overweight and obesity in the united states, 1999-2004. JAMA 2006; 295:1549-55. 5. Dixon AE, Holguin F, Sood A, Salome CM, Pratley RE, Beuther DA, et al. An official American Thoracic Society workshop report: obesity and asthma. Proc Am Thorac Soc 2010;7:325-35. 6. Forno E, Acosta-Perez E, Brehm J, Han YY, Alvarez M, Colon-Semidey A, et al. Obesity and adiposity indicators, asthma, and atopy in Puerto Rican children. J Allergy Clin Immunol 2014;133:1308-14, e1-5. 7. Forno E, Lescher R, Strunk R, Weiss S, Fuhlbrigge A, Celedon JC, et al. Decreased response to inhaled steroids in overweight and obese asthmatic children. J Allergy Clin Immunol 2011;127:741-9. 8. Borrell LN, Nguyen EA, Roth LA, Oh SS, Tcheurekdjian H, Sen S, et al. Childhood obesity and asthma control in the GALA II and SAGE II studies. Am J Respir Crit Care Med 2013;187:697-702. 9. Lee JM, Okumura MJ, Davis MM, Herman WH, Gurney JG. Prevalence and determinants of insulin resistance among U.S. adolescents: a population-based study. Diabetes Care 2006;29:2427-32. 10. Weiss R, Dziura J, Burgert TS, Tamborlane WV, Taksali SE, Yeckel CW, et al. Obesity and the metabolic syndrome in children and adolescents. N Engl J Med 2004;350:2362-74. 11. Agrawal A, Mabalirajan U, Ahmad T, Ghosh B. Emerging interface between metabolic syndrome and asthma. Am J Respir Cell Mol Biol 2011;44:270-5. 12. Cottrell L, Neal WA, Ice C, Perez MK, Piedimonte G. Metabolic abnormalities in children with asthma. Am J Respir Crit Care Med 2011;183:441-8. 13. Al-Shawwa BA, Al-Huniti NH, DeMattia L, Gershan W. Asthma and insulin resistance in morbidly obese children and adolescents. J Asthma 2007;44:469-73. 14. Del-Rio-Navarro BE, Castro-Rodriguez JA, Garibay Nieto N, Berber A, Toussaint G, Sienra-Monge JJ, et al. Higher metabolic syndrome in obese asthmatic compared to obese nonasthmatic adolescent males. J Asthma 2010;47:501-6. 15. Leone N, Courbon D, Thomas F, Bean K, Jego B, Leynaert B, et al. Lung function impairment and metabolic syndrome: the critical role of abdominal obesity. Am J Respir Crit Care Med 2009;179:509-16. 16. Lee EJ, In KH, Ha ES, Lee KJ, Hur GY, Kang EH, et al. Asthma-like symptoms are increased in the metabolic syndrome. J Asthma 2009;46:339-42. 17. Centers for Disease Control and Prevention. 2007-2008 national health and nutrition examination survey (NHANES) survey operations manuals, brochures, consent documents. 2012. Available at: http://www.cdc.gov/nchs/nhanes/nhanes2007-2008/ current_nhanes_07_08.htm. Accessed February 18, 2014. 18. Lohman TG, Roche AF. Anthropometric standardization reference manual. Champaign (IL): Human Kinetics Books; 1988. 19. Centers for Disease Control and Prevention. A SAS program for the CDC growth charts. 2011. Available at: http://www.cdc.gov/nccdphp/dnpao/growthcharts/resources/sas. htm. Accessed February 18, 2014. 20. Slaughter MH, Lohman TG, Boileau RA, Horswill CA, Stillman RJ, Van Loan MD, et al. Skinfold equations for estimation of body fatness in children and youth. Hum Biol 1988;60:709-23. 21. Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, et al. Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab 2000;85:2402-10. 22. Wallace TM, Levy JC, Matthews DR. Use and abuse of HOMA modeling. Diabetes Care 2004;27:1487-95. 23. Ostchega Y, Prineas RJ, Paulose-Ram R, Grim CM, Willard G, Collins D. National health and nutrition examination survey 1999-2000: effect of observer training and protocol standardization on reducing blood pressure measurement error. J Clin Epidemiol 2003;56:768-74.
FORNO ET AL 7
24. de Ferranti SD, Gauvreau K, Ludwig DS, Neufeld EJ, Newburger JW, Rifai N. Prevalence of the metabolic syndrome in American adolescents: findings from the third national health and nutrition examination survey. Circulation 2004; 110:2494-7. 25. Miller MR, Hankinson J, Brusasco V, Burgos F, Casaburi R, Coates A, et al. Standardisation of spirometry. Eur Respir J 2005;26:319-38. 26. Lecube A, Sampol G, Munoz X, Lloberes P, Hernandez C, Simo R. Insulin resistance is related to impaired lung function in morbidly obese women: a casecontrol study. Diabetes Metab Res Rev 2010;26:639-45. 27. Lim SY, Rhee EJ, Sung KC. Metabolic syndrome, insulin resistance and systemic inflammation as risk factors for reduced lung function in Korean nonsmoking males. J Korean Med Sci 2010;25:1480-6. 28. Tantisira KG, Litonjua AA, Weiss ST, Fuhlbrigge AL. Childhood Asthma Management Program Research Group. Association of body mass with pulmonary function in the childhood asthma management program (CAMP). Thorax 2003; 58:1036-41. 29. Vo P, Makker K, Matta-Arroyo E, Hall CB, Arens R, Rastogi D. The association of overweight and obesity with spirometric values in minority children referred for asthma evaluation. J Asthma 2013;50:56-63. 30. Noveral JP, Bhala A, Hintz RL, Grunstein MM, Cohen P. Insulin-like growth factor axis in airway smooth muscle cells. Am J Physiol Lung Cell Mol Physiol 1994;267:L761-5. 31. Dekkers BG, Schaafsma D, Tran T, Zaagsma J, Meurs H. Insulin-induced laminin expression promotes a hypercontractile airway smooth muscle phenotype. Am J Respir Cell Mol Biol 2009;41:494-504. 32. Cohen P, Noveral JP, Bhala A, Nunn SE, Herrick DJ, Grunstein MM. Leukotriene d4 facilitates airway smooth muscle cell proliferation via modulation of the IGF axis. Am J Physiol Lung Cell Mol Physiol 1995;269:L151-7. 33. Sood A, Shore SA. Adiponectin, leptin, and resistin in asthma: basic mechanisms through population studies. J Allergy (Cairo) 2013;2013:785835. 34. Kattan M, Kumar R, Bloomberg GR, Mitchell HE, Calatroni A, Gergen PJ, et al. Asthma control, adiposity, and adipokines among inner-city adolescents. J Allergy Clin Immunol 2010;125:584-92. 35. Nagel G, Koenig W, Rapp K, Wabitsch M, Zoellner I, Weiland SK. Associations of adipokines with asthma, rhinoconjunctivitis, and eczema in German schoolchildren. Pediatr Allergy Immunol 2009;20:80-1. 36. Mark AL. Selective leptin resistance revisited. Am J Physiol Regul Integr Comp Physiol 2013;305:R566-81. 37. Nasrallah MP, Ziyadeh FN. Overview of the physiology and pathophysiology of leptin with special emphasis on its role in the kidney. Semin Nephrol 2013;33: 54-65. 38. Jiang S, Park DW, Tadie JM, Gregoire M, Deshane J, Pittet JF, et al. Human resistin promotes neutrophil proinflammatory activation and neutrophil extracellular trap formation and increases severity of acute lung injury. J Immunol 2014;192:4795-803. 39. Mishra A, Wang M, Schlotman J, Nikolaidis NM, DeBrosse CW, Karow ML, et al. Resistin-like molecule-beta is an allergen-induced cytokine with inflammatory and remodeling activity in the murine lung. Am J Physiol Lung Cell Mol Physiol 2007;293:L305-13. 40. Kim KM, Kim SS, Lee SH, Song WJ, Chang YS, Min KU, et al. Association of insulin resistance with bronchial hyperreactivity. Asia Pac Allergy 2014;4: 99-105. 41. Sarlus H, Wang X, Cedazo-Minguez A, Schultzberg M, Oprica M. Chronic airway-induced allergy in mice modifies gene expression in the brain toward insulin resistance and inflammatory responses. J Neuroinflam 2013;10:99. 42. Brumpton BM, Camargo CA Jr, Romundstad PR, Langhammer A, Chen Y, Mai XM. Metabolic syndrome and incidence of asthma in adults: the HUNT study. Eur Respir J 2013;42:1495-502. 43. Fimognari FL, Pasqualetti P, Moro L, Franco A, Piccirillo G, Pastorelli R, et al. The association between metabolic syndrome and restrictive ventilatory dysfunction in older persons. J Gerontology A Biol Sci Med Sci 2007;62:760-5. 44. Nakajima K, Kubouchi Y, Muneyuki T, Ebata M, Eguchi S, Munakata H. A possible association between suspected restrictive pattern as assessed by ordinary pulmonary function test and the metabolic syndrome. Chest 2008;134: 712-8. 45. van Huisstede A, Cabezas MC, Birnie E, van de Geijn GJ, Rudolphus A, Mannaerts G, et al. Systemic inflammation and lung function impairment in morbidly obese subjects with the metabolic syndrome. J Obes 2013;2013:131349. 46. Leishangthem GD, Mabalirajan U, Singh VP, Agrawal A, Ghosh B, Dinda AK. Ultrastructural changes of airway in murine models of allergy and diet-induced metabolic syndrome. ISRN Allergy 2013;2013:261297. 47. Rojas-Dotor S, Segura-Mendez NH, Miyagui-Namikawa K, Mondragon-Gonzalez R. Expression of resistin, CXCR3, IP-10, CCR5 and MIP-1alpha in obese patients with different severity of asthma. Biol Res 2013;46:13-20.
8 FORNO ET AL
48. Deiuliis JA, Oghumu S, Duggineni D, Zhong J, Rutsky J, Banerjee A, et al. Cxcr3 modulates obesity-induced visceral adipose inflammation and systemic insulin resistance. Obesity 2014;22:1264-74. 49. Hsiao FC, Wu CZ, Su SC, Sun MT, Hsieh CH, Hung YJ, et al. Baseline forced expiratory volume in the first second as an independent predictor of development of the metabolic syndrome. Metabolism 2010;59: 848-53.
J ALLERGY CLIN IMMUNOL nnn 2015
50. Ford ES, Li C, Zhao G, Pearson WS, Mokdad AH. Prevalence of the metabolic syndrome among U.S. adolescents using the definition from the International Diabetes Federation. Diabetes Care 2008;31:587-9. 51. Agudelo GM, Bedoya G, Estrada A, Patino FA, Munoz AM, Velasquez CM. Variations in the prevalence of metabolic syndrome in adolescents according to different criteria used for diagnosis: which definition should be chosen for this age group? Metab Syndr Relat Disord 2014;12:202-9.
J ALLERGY CLIN IMMUNOL VOLUME nnn, NUMBER nn
FIG E1. Derivation of the study sample from NHANES 2007-2008 and 20092010.
FORNO ET AL 8.e1
8.e2 FORNO ET AL
J ALLERGY CLIN IMMUNOL nnn 2015
FIG E2. Predicted FEV1 by asthma and MS status. All models were adjusted for age, sex, race/ethnicity, health insurance coverage, family history of asthma, environmental tobacco smoke exposure, fasting hours, CRP levels, and z scores for each adiposity indicator (A, BMI; B, PBF; C, WC; D, WHtR). No asthma and no MS, n 5 496; MS only, n 5 58; asthma only, n 5 23; and MS and asthma, n 5 7. *P < .05 compared with the control group (no asthma and no MS).
FORNO ET AL 8.e3
J ALLERGY CLIN IMMUNOL VOLUME nnn, NUMBER nn
TABLE E1. Characteristics of study participants by ‘‘ever asthma’’ status Characteristics
No asthma (n 5 1334)
Ever asthma (n 5 315)
Age (y) 14.54 6 0.05 14.82 6 0.12 Male sex 688 (50.03) 177 (53.95) Race/ethnicity Non-Hispanic white 439 (60.51) 106 (61.68) Non-Hispanic black 294 (13.34) 90 (16.07) Hispanic 530 (19.55) 98 (14.79) Other 71 (6.61) 21 (7.47) Household income
21.68 (26.26 to 21.70 (26.33 to NA 22.85 (28.79 to 2.07 (21.32 to
2.90) 2.93) 3.08) 5.45)
534.77) 552.71) 944.14) 186.53)
Data are presented as b coefficients (95% CIs). Values in boldface indicate statistical significance. All models were adjusted for age, sex, race/ethnicity, health insurance coverage, family history of asthma, environmental tobacco smoke exposure, fasting hours, and CRP levels. SBP, Systolic blood pressure. *Defined on the basis of BMI z scores (additionally adjusted for current asthma). All asthmatic patients had fasting glucose levels of less than 110 mg/dL, and all children of normal weight had WC values of less than the 75th percentile. àP < .05. §P < .01.
FORNO ET AL 8.e5
8.e6 FORNO ET AL
J ALLERGY CLIN IMMUNOL nnn 2015
TABLE E4. Insulin sensitivity/resistance, MS, and percent predicted FEV1, FVC, and FEV1/FVC ratio by asthma status among white, black, and Mexican American participants Outcome
All Participants (n 5 446) FEV1 (% predicted)à FVC (% predicted)à FEV1/FVC ratio (% predicted)à No asthma (n 5 421) FEV1 (% predicted)à FVC (% predicted)à FEV1/FVC ratio (% predicted)à Current asthma (n 5 25) FEV1 (% predicted)à FVC (% predicted)à FEV1/FVC ratio (% predicted)à
G/I ratio
QUICKI value*
HOMA-IR value
MS
3.11 (0.90 to 5.33)k 2.75 (0.73 to 4.76)k
