Peak flow variability in childhood and body mass index in adult life
2018; Elsevier BV; Volume: 143; Issue: 3 Linguagem: Inglês
10.1016/j.jaci.2018.10.035
ISSN1097-6825
AutoresEnrico Lombardi, Debra A. Stern, Duane L. Sherrill, Wayne J. Morgan, Anne L. Wright, Judith García‐Aymerich, Ignasi Serra Pons, Stefano Guerra, Fernando D. Martínez,
Tópico(s)Physical Activity and Health
ResumoSeveral cross-sectional studies have shown an association between obesity and asthma, and meta-analyses of prospective studies have concluded that obesity precedes the development of asthma and wheezing in both adults and children.1Egan K.B. Ettinger A.S. Bracken M.B. Childhood body mass index and subsequent physician-diagnosed asthma: a systematic review and meta-analysis of prospective cohort studies.BMC Pediatr. 2013; 13: 121Crossref PubMed Scopus (77) Google Scholar However, a recent study of 2 longitudinal cohorts suggested that children with asthma are at increased risk of subsequent development of obesity.2Chen Z. Salam M.T. Alderete T.L. Habre R. Bastain T.M. Berhane K. et al.Effects of childhood asthma on the development of obesity among school-aged children.Am J Respir Crit Care Med. 2017; 195: 1181-1188Crossref PubMed Scopus (36) Google Scholar The nature and the direction of the association between the 2 conditions thus remain unclear.3Stukus D.R. Obesity and asthma: the chicken or the egg?.J Allergy Clin Immunol. 2015; 135: 894-895Abstract Full Text Full Text PDF PubMed Scopus (5) Google Scholar Diurnal peak flow variability (PFvar) is considered an important measurement of airway lability in the screening and diagnosis of asthma in population-based studies. We have previously found increased PFvar and response to albuterol in girls who became overweight or obese during the school years as compared with those who did not,4Castro-Rodriguez J.A. Holberg C.J. Morgan W.J. Wright A.L. Martinez F.D. Increased incidence of asthmalike symptoms in girls who become overweight or obese during the school years.Am J Respir Crit Care Med. 2001; 163: 1344-1349Crossref PubMed Google Scholar suggesting a link between an abnormal regulation of the airway tone and weight gain in the school years. The aim of the current study was to determine whether airway lability in children is associated with subsequent body mass index (BMI) increase up to young adult life in the longitudinal Tucson Children's Respiratory Study birth cohort.5Taussig L.M. Wright A.L. Morgan W.J. Harrison H.R. Ray C.G. The Tucson Children's Respiratory Study, I: design and implementation of a prospective study of acute and chronic respiratory illness in children.Am J Epidemiol. 1989; 129: 1219-1231Crossref PubMed Google Scholar Detailed methods and definitions are reported in this article's Online Repository at www.jacionline.org. Questionnaires on participants' respiratory symptoms and physical activity were completed by their parents at age 11 years (Yr11: mean ± SD, 10.7 ± 0.5 years) and 16 years (Yr16: 16.6 ± 0.6 years) and by participants themselves at age 22 years (Yr22: 22.1 ± 0.9 years) and 26 years (Yr26: 26.5 ± 0.9 years). At each survey, skin prick tests were performed and BMI (kg/m2) was calculated. At a mean age of 10.8 years, peak expiratory flow data were collected for 1 week and PFvar was calculated as previously reported6Stein R.T. Holberg C.J. Morgan W.J. Wright A.L. Lombardi E. Taussig L. et al.Peak flow variability, methacholine responsiveness and atopy as markers for detecting different wheezing phenotypes in childhood.Thorax. 1997; 52: 946-952Crossref PubMed Google Scholar (see this article's Online Repository at www.jacionline.org). BMI and PFvar values were logarithmically transformed (base 10) to obtain a normal distribution. The potential confounding effects of parental education, ethnicity, metabolic equivalents of task, and smoke exposure were also tested. This study was approved by the Human Subjects Committee at the University of Arizona, and informed consent was obtained from the parents before age 18 years and, subsequently, from the participants themselves. Among the 600 children with PFvar information at Yr11, the number of subjects with subsequent BMI information was 500 at Yr16, 485 at Yr22, and 453 at Yr26 (see this article's Online Repository at www.jacionline.org). The characteristics of study subjects at Yr11 in comparison with those who were excluded because of missing information on BMI or PFvar (see Table E1 in this article's Online Repository at www.jacionline.org) and at each subsequent survey (see Table E2 in this article's Online Repository at www.jacionline.org) are reported in this article's Online Repository at www.jacionline.org. At Yr11, the geometric mean (95% CI) PFvar was 7.6% (7.3-8.0), range 0.7% to 41.9%, and 62 children (10.3%) had a high PFvar. In univariate analyses, a significant linear correlation was found between log-transformed PFvar at Yr11 and log-transformed BMI at Yr11 (r = 0.10; P = .015), Yr16 (r = 0.14; P = .001), Yr22 (r = 0.14; P = .002), and Yr26 (r = 0.11; P = .019). Fig E1 (in this article's Online Repository at www.jacionline.org) shows that, as compared with participants with normal PFvar, those with high PFvar at Yr11 were more likely to be obese at Yr16 (risk ratio [RR] [95% CI] from generalized estimating equation models, 2.00 [1.17-3.39]), Yr22 (2.31 [1.53-3.48]), and Yr26 (1.77 [1.23-2.56]), but not at Yr11 (1.38 [0.75- 2.54]). Similarly, no significant association was found between high PFvar at Yr11 and obesity earlier in childhood (see this article's Online Repository at www.jacionline.org). In random-effects multivariate models with log-transformed BMI as the continuous dependent variable, we found subjects with high PFvar to have only marginally increased BMI at Yr11 but significantly higher BMI at all other ages compared with subjects with normal PFvar, with the largest differences achieved by Yr26 (Fig E2). The best-fitting model (see Table E3 in this article's Online Repository at www.jacionline.org) included both age and age-squared terms, consistent with deceleration of BMI increase from childhood to young adult life; also (Table E3), the effect of high PFvar on subsequent BMI was mainly explained by the interaction between high PFvar and age, indicating that after adjustment for covariates, high PFvar at Yr11 was associated with a steeper increase over time (or, in other words, a higher velocity of growth) in BMI. After further adjustment of the model for BMI at Yr11 and/or smoke exposure, results remained virtually unchanged (data not shown). However, when random-effects models were stratified by sex, we found notable differences in the association between PFvar and BMI increase between females and males (Table I and Fig 1). The interaction between high PFvar and age was highly significant in females (P < .001) but not in males (P = .968), showing that the effect of high PFvar on the subsequent BMI increase was present only in the former group (Table I). These effects were confirmed when repeating the analysis in subjects who were neither overweight/obese nor had asthma at Yr11 (see this article's Online Repository at www.jacionline.org).Table ILongitudinal analysis by random-effects models of base 10 log-transformed BMI at Yr11, Yr16, Yr22, and Yr26 as the dependent variable and potential confounders at the 4 surveys stratified by sex∗Age was centered at the youngest age, 9.2 y, to facilitate interpretation of the main effect coefficient of PFvar.Independent variablesFemales (n = 308; obs = 898)Males (n = 276; obs = 772)Coefficient95% CIP valueCoefficient95% CIP valueHigh PFvar at Yr110.018−0.019 to 0.054.3380.018−0.014 to 0.050.275Age0.0180.016 to 0.020<.0010.0200.017 to 0.022<.001Age2Chen Z. Salam M.T. Alderete T.L. Habre R. Bastain T.M. Berhane K. et al.Effects of childhood asthma on the development of obesity among school-aged children.Am J Respir Crit Care Med. 2017; 195: 1181-1188Crossref PubMed Scopus (36) Google Scholar−0.0005−0.0006 to −0.0004<.001−0.0005−0.0006 to −0.0004<.001High PFvar × Age0.0030.001 to 0.005<.0010.0004−0.002 to 0.002.968Concurrent asthma0.0200.007 to 0.033.002−0.002−0.011 to 0.014.802Concurrent atopy−0.006−0.016 to 0.005.312−0.003−0.015 to 0.009.649Concurrent METs†METs were used as an ordinal variable at each survey where the first category was 0 MET h/wk, the second category was between 1 and 40 MET h/wk, and the third category was >40 MET h/wk (see text).−0.002−0.006 to 0.003.503−0.004−0.008 to 0.0009.114Maternal education ≤12 y0.020−0.004 to 0.047.095−0.0008−0.024 to 0.022.948Paternal education ≤12 y0.015−0.012 to 0.042.2760.0340.011 to 0.057.004At least 1 Hispanic parent‡Reference category: both non-Hispanic white parents.0.013−0.012 to 0.037.3060.018−0.003 to 0.040.097Other ethnicity‡Reference category: both non-Hispanic white parents.0.0330.003 to 0.064.032−0.013−0.043 to 0.017.398MET, Metabolic equivalent of task; obs, observations.∗ Age was centered at the youngest age, 9.2 y, to facilitate interpretation of the main effect coefficient of PFvar.† METs were used as an ordinal variable at each survey where the first category was 0 MET h/wk, the second category was between 1 and 40 MET h/wk, and the third category was >40 MET h/wk (see text).‡ Reference category: both non-Hispanic white parents. Open table in a new tab MET, Metabolic equivalent of task; obs, observations. In summary, we found high PFvar at Yr11 to predict a steeper subsequent BMI increase up to young adult life independent of multiple covariates, including current asthma at Yr11. Interestingly, the effects of high PFvar at Yr11 were much stronger on subsequent than on concomitant levels of BMI, suggesting that our findings are very unlikely to be explained by concomitant effects of obesity on airway lability. In other words, if the relationship between PFvar and BMI were simply explained by the fact that subjects with a higher BMI are deconditioned and have a lower lung function, one might expect a better correlation between high PFvar and elevated BMI during childhood surveys, which was not the case in our study. These findings support the possibility that the association between asthma and obesity may be bidirectional or due to shared factors that preexist both conditions3Stukus D.R. Obesity and asthma: the chicken or the egg?.J Allergy Clin Immunol. 2015; 135: 894-895Abstract Full Text Full Text PDF PubMed Scopus (5) Google Scholar (for previous literature and the limitations of the study, see this article's Online Repository at www.jacionline.org). Obesity and asthma are known to share common genetic mechanisms7Gonzalez J.R. Caceres A. Esko T. Cusco I. Puig M. Esnaola M. et al.A common 16p11.2 inversion underlies the joint susceptibility to asthma and obesity.Am J Hum Genet. 2014; 94: 361-372Abstract Full Text Full Text PDF PubMed Scopus (37) Google Scholar, 8Permaul P. Kanchongkittiphon W. Phipatanakul W. Childhood asthma and obesity–what is the true link?.Ann Allergy Asthma Immunol. 2014; 113: 244-246Abstract Full Text Full Text PDF PubMed Scopus (25) Google Scholar; furthermore, developmental and early-life factors have been suggested as affecting the development of both obesity and asthma.8Permaul P. Kanchongkittiphon W. Phipatanakul W. Childhood asthma and obesity–what is the true link?.Ann Allergy Asthma Immunol. 2014; 113: 244-246Abstract Full Text Full Text PDF PubMed Scopus (25) Google Scholar, 9Litonjua A.A. Gold D.R. Asthma and obesity: common early-life influences in the inception of disease.J Allergy Clin Immunol. 2008; 121: 1075-1084Abstract Full Text Full Text PDF PubMed Scopus (96) Google Scholar We propose that such common factors might cause both an alteration in the regulation of airway tone and a greater tendency to gain weight during the growing years and that the asthma-related phenotype might thus precede the obesity phenotype. We gratefully acknowledge the contributions of Dr Lynn Taussig, who started the Tucson Children's Respiratory Study in 1980. We thank Bruce Saul for data management and our study nurses, Marilyn Lindell, Lydia de la Ossa, Nicole Pargas, and Silvia S. Lopez, for data collection and participant follow-up. We thank the CRS subjects for their continued participation in the study. The Tucson Children's Respiratory Study (CRS) is a longitudinal cohort study involving 1246 children enrolled at birth between May 1980 and October 1984.E1Taussig L.M. Wright A.L. Morgan W.J. Harrison H.R. Ray C.G. The Tucson Children's Respiratory Study, I: design and implementation of a prospective study of acute and chronic respiratory illness in children.Am J Epidemiol. 1989; 129: 1219-1231Crossref PubMed Scopus (169) Google Scholar Questionnaires on participants' respiratory symptoms were completed by their parents at Yr11 (mean ± SD, 10.7 ± 0.5 years) and Yr16 (mean ± SD, 16.6 ± 0.6 years) and by participants themselves at Yr22 (mean ± SD, 22.1 ± 0.9 years) and Yr26 (mean ± SD, 26.5 ± 0.9 years). At each survey, current asthma was defined as a positive report of asthma diagnosed by a physician, with at least 1 asthma attack or wheezing episode during the previous year. Information on sport-related physical activity during the previous year was also collected at each survey using the questions: "Is this child a member of a sports team?" at Yr11; "Does this child participate in sports?" at Yr16; and "During the past year, have you exercised regularly?" at Yr22 and Yr26. Metabolic equivalents of task (METs) were calculated according to Ainsworth et alE2Ainsworth B.E. Haskell W.L. Herrmann S.D. Meckes N. Bassett Jr., D.R. Tudor-Locke C. et al.2011 Compendium of Physical Activities: a second update of codes and MET values.Med Sci Sports Exerc. 2011; 43: 1575-1581Crossref PubMed Scopus (3823) Google Scholar using the questions "If yes, in which sports? (hours per week)" at Yr11 and Yr16 and "Which type of exercise have you done regularly? (hours per week)" at Yr22 and Yr26. METs were categorized into 3 levels, namely, 1, "0 MET hours per week"; 2, "1 to 40 MET hours per week"; and 3, "more than 40 MET hours per week." Skin prick tests were performed at each survey. For the sake of consistency, in this study we considered only the 6 aeroallergens that were tested in all 4 surveys: Bermuda grass, careless weed, olive, mesquite, mulberry, and Alternaria alternata (Hollister-Stier Laboratories, Everett, Washington, DC). Histamine and a negative control consisting of 50% glycerin were also applied. Tests were read at 20 minutes, and the size of the wheal elicited by each allergen was recorded as the longest diameter plus the perpendicular diameter (in mm). Wheal sizes of 3 mm or greater, after subtracting the negative control, were considered positive. A subject was considered atopic if he or she had a positive skin test reaction to at least 1 aeroallergen. Study nurses used beam balance scales at each survey to measure weight (Health-o-meter; Continental Scale Corp, Chicago, Ill). Height was measured with the subject barefoot and erect against a vertical backboard or wall-mounted scale (Accustat-Stadiometer; Genentech, San Francisco, Calif). Self-reported weight and height were also available from the questionnaires. BMI was calculated as weight (kg)/square of height (m2). Subjects were considered to be "obese" if their BMI was greater than or equal to 95th percentile of Centers for Disease Control and Prevention's 2000 US sex- and age-standardized values up to the age of 17 yearsE3Kuczmarski R.J. Ogden C.L. Guo S.S. Grummer-Strawn L.M. Flegal K.M. Mei Z. et al.2000 CDC Growth Charts for the United States: methods and development.Vital Health Stat. 2002; 11: 1-190Google Scholar or if their BMI was greater than or equal to 30 kg/m2 over age 17 years.E4Ogden C.L. Carroll M.D. Curtin L.R. McDowell M.A. Tabak C.J. Flegal K.M. Prevalence of overweight and obesity in the United States, 1999-2004.JAMA. 2006; 295: 1549-1555Crossref PubMed Scopus (7397) Google Scholar Subjects were considered to be "overweight/obese" if their BMI was greater than or equal to 85th percentile of Centers for Disease Control and Prevention's 2000 US sex- and age-standardized values up to the age of 17 yearsE3Kuczmarski R.J. Ogden C.L. Guo S.S. Grummer-Strawn L.M. Flegal K.M. Mei Z. et al.2000 CDC Growth Charts for the United States: methods and development.Vital Health Stat. 2002; 11: 1-190Google Scholar or if their BMI was greater than or equal to 25 kg/m2 over age 17 years.E4Ogden C.L. Carroll M.D. Curtin L.R. McDowell M.A. Tabak C.J. Flegal K.M. Prevalence of overweight and obesity in the United States, 1999-2004.JAMA. 2006; 295: 1549-1555Crossref PubMed Scopus (7397) Google Scholar At a mean age of 10.8 years, subjects were trained by study nurses on the use of a home peak expiratory flow (PEF) meter ("Mini-Wright"; Clements Clarke International, Harlow, Essex, United Kingdom) and parents were asked to record on a diary the best of 3 attempts of their children in the morning, in the afternoon, and before going to bed at night. PEF data were collected for 1 week. To avoid the "learning effect" on PEF measurements, as previously reported,E5Stein R.T. Holberg C.J. Morgan W.J. Wright A.L. Lombardi E. Taussig L. et al.Peak flow variability, methacholine responsiveness and atopy as markers for detecting different wheezing phenotypes in childhood.Thorax. 1997; 52: 946-952Crossref PubMed Scopus (252) Google Scholar measures from the first morning of the PEF recording period were eliminated from the analysis. Only those subjects who recorded PEF measurements at least twice a day for 4 days or more (after exclusion of the first morning of the recording period) were included in the analysis.E5Stein R.T. Holberg C.J. Morgan W.J. Wright A.L. Lombardi E. Taussig L. et al.Peak flow variability, methacholine responsiveness and atopy as markers for detecting different wheezing phenotypes in childhood.Thorax. 1997; 52: 946-952Crossref PubMed Scopus (252) Google Scholar, E6Higgins B.G. Britton J.R. Chinn S. Cooper S. Burney P.G. Tattersfield A.E. Comparison of bronchial reactivity and peak expiratory flow variability measurements for epidemiologic studies.Am Rev Respir Dis. 1992; 145: 588-593Crossref PubMed Scopus (95) Google Scholar The amplitude percent meanE5Stein R.T. Holberg C.J. Morgan W.J. Wright A.L. Lombardi E. Taussig L. et al.Peak flow variability, methacholine responsiveness and atopy as markers for detecting different wheezing phenotypes in childhood.Thorax. 1997; 52: 946-952Crossref PubMed Scopus (252) Google Scholar, E7Siersted H.C. Hansen H.S. Hansen N.C. Hyldebrandt N. Mostgaard G. Oxhoj H. Evaluation of peak expiratory flow variability in an adolescent population sample. The Odense Schoolchild Study.Am J Respir Crit Care Med. 1994; 149: 598-603Crossref PubMed Scopus (46) Google Scholar was chosen as the PFvar index and defined as follows:PFvar=∑(maximumdailyPEF−minimumdailyPEF)/meandailyPEFNumberofdaysintheperiod×100 Consistent with our previous report, high PFvar at Yr11 was defined as a PFvar value greater than or equal to 16.6% (ie, above the 90th percentile of a healthy reference subgroup of subjects whose skin test results were negative, who had never wheezed, nor had been diagnosed as having asthma).E5Stein R.T. Holberg C.J. Morgan W.J. Wright A.L. Lombardi E. Taussig L. et al.Peak flow variability, methacholine responsiveness and atopy as markers for detecting different wheezing phenotypes in childhood.Thorax. 1997; 52: 946-952Crossref PubMed Scopus (252) Google Scholar The potential confounding effects of parental education, ethnicity, and smoke exposure were also tested. Maternal and paternal education was assessed at child's enrollment in the CRS and categorized into "12 years or more" and "12 years or less." Ethnicity was categorized on the basis of ethnicity of parents ("both parents non-Hispanic white," "at least 1 Hispanic white parent," or "other"). For smoke exposure, 4 combination groups were generated on the basis of parental smoking at child's birth and active participant smoking between Yr16 and Yr26, as previously reportedE8Guerra S. Stern D.A. Zhou M. Sherrill D.L. Wright A.L. Morgan W.J. et al.Combined effects of parental and active smoking on early lung function deficits: a prospective study from birth to age 26 years.Thorax. 2013; 68: 1021-1028Crossref PubMed Scopus (77) Google Scholar: "No parental smoking/No active smoking," "No parental smoking/Yes active smoking," "Yes parental smoking/No active smoking," and "Yes parental smoking/Yes active smoking." This study was approved by the Human Subjects Committee at the University of Arizona, and informed consent was obtained from the parents before age 18 years and, subsequently, from the participants themselves. BMI and PFvar values were logarithmically transformed (base 10) to obtain a normal distribution. Student t test and the chi-square test were used for comparisons of means and proportions, respectively. Linear regression was used in the univariate analysis to assess the relation between PFvar at Yr11 and BMI at Yr11, Yr16, Yr22, and Yr26, respectively. To adjust for the intrasubject serial correlation of repeated observations, generalized estimating equations were used to assess the association between dichotomous PFvar at Yr11 and the categorical variable indicating obese subjects at each of the 4 surveys,E9Brown H. Prescott R. Applied Mixed Models in Medicine, Statistics in Practice. John Wiley & Sons, Chichester, UK1999Google Scholar and random-effects models were used for all the multivariate longitudinal analyses with log-transformed BMI as the continuous dependent variable.E9Brown H. Prescott R. Applied Mixed Models in Medicine, Statistics in Practice. John Wiley & Sons, Chichester, UK1999Google Scholar In the random-effects models, age was centered to the youngest child included at Yr11. Linear contrast was used to estimate survey-specific effects. Stata version 14.0 was used for analysis. Potential confounders of the relationship between PFvar and BMI were included in the models a priori. Statistical significance was tested at a 2-sided alpha level of 0.05. PEF variability information at Yr11 was collected for 600 children, and all children had 1 or more BMI measurements. BMI was assessed by the study nurses at Yr11 (n = 599), Yr16 (n = 448), Yr22 (n = 397), and Yr26 (n = 336). Excellent correlation was found between BMI assessed by the study nurses and questionnaire-reported BMI (Spearman rho, 0.97 at Yr11, 0.96 at Yr16, 0.98 at Yr22, and 0.95 at Yr26). For this reason, to minimize the potential selection bias due to missing information at follow-up, questionnaire-reported BMI at Yr16, Yr22, and Yr26 was used for those subjects for whom BMI assessed by the study nurses was missing at those surveys. Thus, among the 600 children with PFvar information at Yr11, the final number of subjects with subsequent BMI information was 500 at Yr16, 485 at Yr22, and 453 at Yr26. The characteristics of the study subjects at each survey are reported in Table I. At Yr11, as compared with participants who were included in the present study, those who were excluded because they had missing information on BMI or PFvar were slightly older, more physically active, and had a higher proportion of non-Hispanic white parents (Table E1). In contrast, no significant differences were found in sex, asthma, atopy, and parental education.Table E1Characteristics of the study subjects at Yr11 (for proportions, the actual numbers are shown in parentheses)∗Totals are not the same for all individual characteristics because information is missing for some subjects.CharacteristicYr11Included (n = 600)Not included (n = 356)Age (y), mean ± SD10.7 ± 0.511.1 ± 0.8†P < .001 by t test vs study subjects at same age.Sex: male, %48.2 (289 of 600)49.7 (177 of 356)Current asthma, %17.6 (104 of 592)13.2 (46 of 349)‡.1 > P > .05 by χ2 test vs study subjects at same age.Atopy, %54.7 (327 of 598)57.3 (63 of 110)Current physical activity, %51.3 (307 of 599)57.9 (206 of 356)§P < .05 by χ2 test vs study subjects at same age.MET (h/wk), % 048.8 (292 of 598)42.3 (150 of 355)§P < .05 by χ2 test vs study subjects at same age. 1-4025.4 (152 of 598)23.9 (85 of 355) >4025.8 (154 of 598)33.8 (120 of 355)Maternal education ≤12 y, %28.9 (173 of 599)25.6 (91 of 356)Paternal education ≤12 y, %26.2 (154 of 588)23.1 (81 of 351)Parental ethnicity, % Non-Hispanic white60.8 (365 of 600)70.8 (252 of 356)§P < .05 by χ2 test vs study subjects at same age. ≥1 Hispanic white25.8 (155 of 600)16.0 (57 of 356) Other ethnicity13.3 (80 of 600)13.2 (47 of 356)BMI (kg/m2), GM (95% CI)18.6 (18.3-18.9)18.3 (18.0-18.7)Overweight/obese, %27.4 (164 of 599)24.6 (75 of 305)Obese, %12.2 (73 of 599)10.2 (31 of 305)GM, Geometric mean.∗ Totals are not the same for all individual characteristics because information is missing for some subjects.† P < .001 by t test vs study subjects at same age.‡ .1 > P > .05 by χ2 test vs study subjects at same age.§ P < .05 by χ2 test vs study subjects at same age. Open table in a new tab Table E2Characteristics of the study subjects at Yr11, Yr16, Yr22, and Yr26 (for proportions, the actual numbers are shown in parentheses)∗Totals are not the same for all individual characteristics because information is missing for some subjects.CharacteristicYr11 (n = 600)Yr16 (n = 500)Yr22 (n = 485)Yr26 (n = 453)Age (y), mean ± SD10.7 ± 0.516.6 ± 0.622.1 ± 0.926.5 ± 0.9Sex: male, %48.2 (289 of 600)49.2 (246 of 500)46.6 (226 of 485)46.1 (209 of 453)Current asthma, %17.6 (104 of 592)19.4 (95 of 489)18.7 (90 of 482)20.9 (92 of 445)Atopy, %54.7 (327 of 598)72.0 (317 of 440)72.6 (278 of 383)72.9 (240 of 329)Current physical activity, %51.3 (307 of 599)57.9 (287 of 496)64.0 (307 of 480)66.6 (297 of 446)METs (h/wk), % 048.8 (292 of 598)40.8 (197 of 483)36.2 (173 of 478)34.5 (147 of 426) 1-4025.4 (152 of 598)17.6 (85 of 483)24.7 (118 of 478)32.4 (138 of 426) >4025.8 (154 of 598)41.6 (201 of 483)39.1 (187 of 478)33.1 (141 of 426)Maternal education ≤12 y, %28.9 (173 of 599)24.6 (123 of 499)25.4 (123 of 485)25.0 (113 of 452)Paternal education ≤12 y, %26.2 (154 of 588)23.4 (115 of 491)25.4 (121 of 477)23.1 (103 of 446)Parental ethnicity Non-Hispanic white60.8 (365 of 600)62.4 (312 of 500)63.1 (306 of 485)64.7 (293 of 453) ≥1 Hispanic white25.8 (155 of 600)24.6 (123 of 500)24.7 (120 of 485)23.6 (107 of 453) Other ethnicity13.3 (80 of 600)13.0 (65 of 500)12.2 (59 of 485)11.7 (53 of 453)BMI (kg/m2), GM (95% CI)18.6 (18.3-18.9)22.5 (22.1-22.9)24.9 (24.4-25.4)26.2 (25.7-26.8)Overweight/obese, %27.4 (164 of 599)23.2 (116 of 500)39.2 (190 of 485)50.8 (230 of 453)Obese, %12.2 (73 of 599)12.4 (62 of 500)17.5 (85 of 485)24.1 (109 of 453)GM, Geometric mean.∗ Totals are not the same for all individual characteristics because information is missing for some subjects. Open table in a new tab Table E3Longitudinal analysis by random-effects model of base 10 log-transformed BMI at Yr11, Yr16, Yr22, and Yr26 as the dependent variable and potential confounders at the 4 surveys (n = 584; obs = 1670)∗Yr11 was centered at the youngest age, 9.2 y, to facilitate interpretation of the main effect coefficient of PFvar.Independent variablesCoefficient95% CIP valueHigh PFvar at Yr110.016−0.008 to 0.041.195Age0.0190.018 to 0.021<.001Age2Chen Z. Salam M.T. Alderete T.L. Habre R. Bastain T.M. Berhane K. et al.Effects of childhood asthma on the development of obesity among school-aged children.Am J Respir Crit Care Med. 2017; 195: 1181-1188Crossref PubMed Scopus (36) Google Scholar−0.0005−0.0006 to −0.0004<.001High PFvar × Age0.0020.0005 to 0.003.008Male sex0.002−0.012 to 0.016.784Concurrent asthma0.010−0.0007 to 0.019.035Concurrent atopy−0.005−0.013 to 0.003.220Concurrent METs†METs were used as an ordinal variable at each survey where the first category was 0 MET h/wk, the second category was between 1 and 40 MET h/wk, and the third category was >40 MET h/wk (see text).−0.003−0.007 to −0.0001.041Maternal education ≤12 y0.011−0.006 to 0.0297.218Paternal education ≤12 y0.0230.005 to 0.041.012≥1 Hispanic parent‡Reference category: both non-Hispanic white parents.0.014−0.003 to 0.030.105Other ethnicity‡Reference category: both non-Hispanic white parents.0.015−0.007 to 0.037.173∗ Yr11 was centered at the youngest age, 9.2 y, to facilitate interpretation of the main effect coefficient of PFvar.† METs were used as an ordinal variable at each survey where the first category was 0 MET h/wk, the second category was between 1 and 40 MET h/wk, and the third category was >40 MET h/wk (see text).‡ Reference category: both non-Hispanic white parents. Open table in a new tab GM, Geometric mean. GM, Geometric mean. Geometric mean (95% CI) BMI values at each survey for the subjects included in the study are also reported in Table I. The proportion of overweight/obese subjects was 27.4% at Yr11, 23.2% at Yr16, 39.2% at Yr22, and 50.8% at Yr26. At Yr11, the geometric mean (95% CI) PFvar was 7.6% (7.3-8.0), range 0.7% to 41.9%, and 62 children (10.3%) had a high PFvar. In univariate analyses, a significant linear correlation was found between log-transformed PFvar at Yr11 and log-transformed BMI at Yr11 (r = 0.10; P = .015), Yr16 (r = 0.14; P = .001), Yr22 (r = 0.14; P = .002), and Yr26 (r = 0.11; P = .019). In addition, as compared with subjects with normal PFvar, those with high PFvar had higher BMI at Yr11 (P = .037), Yr16 (P = .0001), Yr22 (P = .0003), and Yr26 (P = .068). PFvar at Yr11 tended to have a stronger association with subsequent (Yr16, Yr22, and Yr26) than with concomitant (Yr11) BMI. Consistent with this scenario, Fig E1 shows that, as compared with participants with normal PFvar, those with high PFvar at Yr11 were more likely to be obese at Yr16 (RR [95% CI] from generalized estimating equations models, 2.00 [1.17-3.39]), Yr22 (2.31 [1.53-3.48]), and Yr26 (1.77 [1.23-2.56]), but not at Yr11 (1.38 [0.75-2.54]). When we looked for a possible association between high PFvar at Yr11 and obesity earlier in childhood, we found that, similarly to Yr11, at age 6 years (mean, 6.3 yea
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