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Ethnicity, % (n) Irish or any other white background 94.5 (7731) Asian or Asian Irish 2.1 (174) Black or Black Irish 2.9 (240) Other, including mixed background 0.5 (39) irth weight (g), % (n) < 3000 16.7 (1356) 3000每4000 67.7 (5480) > 4000 15.6 (1262) Breastfeeding, % (n) Yes 61.8 (5060) No 38.2 (3125) Chronic illness child, % (n)
BMI body mass index, GP general practitioner
Standard deviation (SD) scores of BMI (BMI SD scores) were calculated for the sample based on the UK-WHO data- set using ※LmsGrowth§, a Microsoft Excel add-in designed for use with growth chart references which provide age- and gender-specifi cut off [26]. Based on BMI SD scores, a sample population can be classified as underweight, normal, overweight and obese depending on how much they deviate from the population norm. The following BMI percentile cuts offs were applied: ≡ 91st percentile for overweight and ≡ 98th percentile for obesity. The study outcome measurements were: (1) body mass as a logistic variable (obese- vs non-obese-dependent variable); (2) BMI SD score as a longitudinal linear dependent variable (longitudinal BMI SD score as a linear dependent variable) at 3 years and 5 years of age. Prevalence of overweight was 20.4% (1748) at 5 years, with girls more likely to be overweight compared to boys [girls: 22.6% (957); boys: 18.2% (791)] (Table 1). Covariate assessment
The following were included as potential confounders/effect modifiers in the analyses and were extracted from the sur- veys as described below. Gender of study child, birthweight, gestational age and length of time of breastfeeding was recorded at 9 months. Information on study child*s diet, physical activity and health was obtained from age-5 questionnaire. A validated food frequency questionnaire measured in-depth dietary pattern data [27]. An estimate of the study child*s daily cal- orie consumption was calculated from the food frequency information using the calories content from typical age- adjusted children*s portion sizes [28] and nutrient values based on the McCance and Widdowson Food Composition Tables [29]. Other questionnaire data extracted at the age-5 survey included: socio-demographic information (social class, mother*s age, maternal education, free general practitioner access, household income, household type, ethnicity), life- style characteristics [cigarette smoking, cr豕che (daycare) attendance] and measured maternal height and weight. Table 1 summarizes the demographic and socioeconomic details. Half of the cohort, 50.7% (4147) were males. The majority of the sample was ※Irish or any other white back- ground§ (94.5%, n = 7731). Data analysis
A cross-sectional description of the cohort was prepared from data collected at 9 months, 3 and 5 years of age. Lon- gitudinal mixed models examined the relationship between antibiotic exposure and the repeated measures of BMI over time. Differences between exposed and unexposed subjects were tested at each of the three time points, with random effects used for subjects to account for individual patterns of weight change. Analyses were carried out using the ※lme4§ package in R [30]. Four sets of longitudinal mixed models were developed using both outcome measurements: (1) obese vs non-obese; (2) longitudinal BMI SD score. The analyses compared body mass trajectory to four diff ent antibiotic exposures: (1) any antibiotic use from 2 to 3 years; (2) number of courses of antibiotics taken between 2 and 3 years; (3) number of courses of antibiotics taken between 4 and 5 years and (4) cumulative antibiotic exposure from 2 to 3 years and 4 to 5 years. The final stage of the analysis involved fitting mod- els with interaction terms (antibiotic use: gender) and strati- fi longitudinal mixed modeling by gender, having been breastfed, maternal BMI and maternal smoking to examine any interaction between covariates. The results reported coeffi standard errors and P values for linear multilevel models or odds ratios (ORs) and 95% CIs for binomial multilevel models. Statistically sig- nifi P values were attained at 汐 = 0.05. We plotted the mixed model results for number of courses of antibiotics used at 2每3 years and body mass trajectory from 9 months to 5 years (coefficients and 95% CIs using coefplot2) to quickly visualize the point estimates and measures of uncertainty of the fitted statistical models [31]. Results
Longitudinal analysis of the association between any antibiotic exposure (yes or no)
and body mass across early childhood (9 months to 5 years) In an unadjusted cross-sectional model, any antibiotic use at age 3 was statistically associated with a substantial increase in BMI SD scores [coeffi + 0.08 BMI SD units, standard error (SE) 0.03, P = 0.001]. In the logistic mixed model using categories obese vs non-obese as the outcome, any antibiotic use was not associated with being obese during early childhood, adjusting for other known predictors (OR 1.07, 95% CI 0.75每1.52) (Table 2). Being obese was independently signifi antly associated with four predictors: Black or Black Irish ethnicity (OR 3.44, 95% CI 1.25每9.47), higher birthweight (OR 1.95, 95% CI 1.46每2.61), lower maternal education (OR 2.52, 95% CI 1.34每4.72) and maternal BMI of ≡ 25 (OR 1.43, 95% CI 1.02每2.61).
Table 2 Longitudinal mixed models of the association between the number of courses of antibiotics use at 2每3 years and/or 4每5 years and body mass trajectory from 9 months to 5 years adjusting for known confoundersa Outcome variable BMI SD-score Obese vs non-obese Coefficient SE P value OR 95% CI (lower limit) 95% CI (upper limit)
BMI body mass index, SD standard deviation, SE standard error, OR odds ratio, CI confidence interval. *P < 0.05. aAdjusted for gender, attend- ing cr豕che, being breastfed, food energy intake (kcal), level of exercise, having a chronic illness, maternal BMI, birthweight of child, social class of household, maternal smoking, maternal education and ethnicity
Longitudinal analysis of the association
between number of courses of antibiotics and body mass across early childhood (9 months to 5 years) In the logistic mixed model, using ≡ 4 courses of antibiot- ics during the 12-month period from 2 to 3 years was sig- nifi y associated with being obese during early child- hood, adjusting for other known predictors (OR 1.60, 95% CI 1.11每2.31) (Table 2). In the multivariable model, being obese was not associated with a higher calorifi diet (OR 1.71, 95% CI 0.79每3.69) or having chronic illness (OR 1.22, 95% CI 0.77每1.94) (Supplementary Table 1). Increased BMI SD score was moderately associated with taking ≡ 4 courses of antibiotics during the 12-month period from 2 to 3 years, adjusting for other known predic- tors (coefficient + 0.09 BMI SD units, SE 0.04, P = 0.037) (Table 2); (unadjusted univariate model: coeffi + 0.24 BMI SD units, SE 0.04, P < 0.001). In contrast, taking ≡ 4 courses of antibiotics during the 12-month period from 4 to 5 years was not signifi- cantly associated with being obese during early childhood, adjusting for other known predictors (OR 1.60, 95% CI 1.11每2.31) (Table 2). Cumulative exposure ≡ 4 courses of antibiotics expo- sures from age 2 to 3 and age 4 to 5 showed the strongest association with increased risk of obesity at age 5 (coef- ficient + 0.12, P < 0.001), having adjusted for other known confounders (Table 2). In the multivariable model, no asso- ciation was found between being obese and having with a higher calorific diet (OR 1.59, 95% CI 0.60每4.23) or having chronic illness (OR 1.05, 95% CI 0.59每1.89) (Supplemen- tary Table 2). Interaction effects of covariates
In a longitudinal mixed model fitted with a gender by anti- biotic use interaction term as well as the covariates above, there was no statistically significant interaction found (gen- der: 4 + courses of antibiotics interaction term: coefficient 每 0.109 BMI SD units, P = 0.303; 4 + courses of antibiot- ics: coefficient 0.18 BMI SD units, P = 0.029). In the mod- els stratifi d by gender, there was no evidence that gender
modified the association between antibiotic use and obesity (4 + courses of antibiotics: coefficient 0.06 BMI SD units, P = 0.329). In the models stratified by breastfeeding, there was a non- statistically signifi trend of increasing BMI SD-score with increasing antibiotic use among those infants whose were breastfed (4 + courses of antibiotics: coefficient 0.12 BMI SD units, P = 0.090). There was a non-statistically significant trend of increas- ing BMI SD score with increasing antibiotic exposure among those infants whose parents did not smoke and had taken four or more courses of antibiotics (4 + courses of antibiotics: coefficient 0.09 BMI SD units, P = 0.054). There was no evidence of having a chronic illness modi- fi the association between antibiotic use and obesity in the stratifi models (4 + courses & having a chronic ill- ness: P = 0.153; 4 + courses and not having a chronic illness: P = 0.128). In a longitudinal mixed model fi with a maternal weight category by antibiotic use interaction term as well as the covariates above, there was no statistically significant interaction found (gender: 4 + courses of antibiotics inter- action term: coeffi − 0.030 BMI SD units, P = 0.774; 4 + courses of antibiotics: coeffi 0.13 BMI SD units, P = 0.082). When the interaction between maternal weight and the antibiotic每obesity association was examined in stratifi analysis, interaction between maternal weight, antibiotic exposure and obesity was observed. Children of normal-weight mothers exposed to ≡ 4 courses of antibiotics between 2 and 3 years showed an increase of + 0.12 BMI SD units (P = 0.035) compared to children of overweight and obese mothers (coefficient + 0.06, P = 0.367). Discussion
In this study, we set out to examine any effect of antibiotic use after the age of 2 years on BMI in childhood in an Irish longitudinal cohort study. Our results demonstrated that the only high levels of antibiotic use of ≡ 4 courses of antibiot- ics between 2 and 3 years were associated with an increased body mass trajectory from 9 months to 5 years. Pooled data from two study waves strengthened the association. No asso- ciation was detected at less than four courses or in cross- sectional analysis of antibiotic exposure between ages 4 and 5. These findings highlight the importance of frequency of antibiotic use and age at administration in the detection of antibiotic-related weight changes. This would suggest that repeated antibiotic exposure may impair recovery of micro- biota and metabolic functioning in young children and that microbiota recovery is possible with less frequent antibiotic exposure. Our findings demonstrated a modest effect size of weight increase following antibiotic exposure. Translated into weight increments for the average 5 years old, a + 0.09 SD unit increase in body mass corresponds to a 250-g increase in weight. A comparable eff size was demon- strated by Schwartz et al. [32] (1 course: + 0.01 BMI SD units, P = 0.009; 4每6 courses: + 0.05 BMI SD units, P value = 0.012). Any effect of antibiotics on weight is unlikely to have significant impact at an individual level. However, the impact at a population level may be more significant. Three studies found no or inconsistent risk of obesity with antibiotic use during childhood [20, 33, 34]. The differences in fi across studies are likely due, in part, to diff - ences in the age of participants and intervals between anti- biotic use and weight measurements, including self-report of child*s weight measurements [33, 34] and cross-sectional designs [34]. Furthermore, it has been suggested that gut microbiota may be less susceptible to alteration by antibiot- ics as it matures between birth and age 3 [35]. As we have no information available on antibiotic use prior to 2 years of age, it is also possible that the high antibiotic use observed in early childhood may be a persistent pattern from infancy that has biased our results. The finding that children of normal-weight mothers exposed to ≡ 4 courses of antibiotics between 2 and 3 years had an increased risk of obesity at age 5 compared to chil- dren of mothers with a BMI > 25 was similar to two stud- ies [14, 33]. One hypothesis is that normal and overweight mothers have diff ent gut microbial composition that is passed on to infants during delivery, predisposing the former to greater microbiota disruption with antibiotics [33, 36, 37]. Another possibility is that risk factors for obesity associated with maternal obesity may mask any relationship between antibiotics and obesity [38]. Obesity is a multifactorial condition. Many factors such as duration of breastfeeding, exercise, and health status have been shown to contribute to the risk of developing obesity. While these covariates did not appear to be statistically asso- ciated with changes in the antibiotic每obesity relationship in the population, it may suggest that antibiotics contribute to the risk of obesity via different biological mechanisms and/ or the antibiotic每obesity relationship does not follow typical socioeconomic patterning of health issues. The main strength of the GUI cohort is that repeated objective measurements of weight and height were collected from study child and mother. Prior studies of antibiotic use and BMI were often limited by lack of follow-up weight measurements. Another advantage GUI is the wide range of available information on confounding social and environ- mental variables collected. Notwithstanding these strengths, our study has some signifi limitations. The study survey instruments were not specifi y designed for this study; therefore, some
desirable pieces of information on antibiotic use such as type, dose and prescribing indication were not available. The GUI study did not record antibiotic usage at 9 months. Recall bias limits the maternal report of antibiotic use, chronic illness diagnosis, length of time breastfed and accuracy of reported smoking behaviours. In addition, BMI SD scores were the only measures available that would allow comparison between ages, as no alternative measure of obesity is available for chil- dren under 2 years of age [39, 40]. Using age-adjusted SD score removes some of the within-person variation in body mass and its use in longitudinal studies has been criticized because of this [41]. Also, it should be noted that the reference charts used in obesity studies change the rate of obesity detected across studies, with the UK-WHO reference charts used here giving higher rates of obesity compared with other cut-off The persistent though modest association between high levels of antibiotic use and obesity, independent of predic- tors such as birthweight and maternal weight, is unlikely to be spurious. Additional studies addressing the biologi- cal mechanism behind the effect of antibiotic exposure and subsequent obesity would be a logical next step for research, including examination of the changes in bacterial gut profile associated with different types and doses of antibiotics. For clinical practice, deployment of new approaches in relation to prescribing practices based on the risk of obesity would be premature as there is insuffi nt evidence to infer that antibiotics cause obesity or are unsafe for children. In conclusion, we found that high levels of antibiotic exposure of ≡ 4 courses of antibiotics between ages 2 and 3 were associated with obesity at age 5. Given the incon- sistency of the eff between age 2每3 and age 4每5, it is likely the association is more complex than postulated in studies to date. The impact of antibiotics on metabolic function was evident after repeated courses of treatment suggesting that recovery is possible with less frequent antibiotic exposure. Given the current epidemic of child- hood obesity and widespread use of antibiotics, further research is needed with a focus on detailed information on the types of antibiotics taken and biological sampling of gut bacteria before and after antibiotic use. Author contributions DK contributed to study design, data analysis, and manuscript writing and review. AK contributed to study design and data analysis. TOD contributed to study design, manuscript writing, and manuscript review. CH contributed to study design, data analysis, and manuscript writing and review. All authors approved the final ver- sion of the manuscript. Funding The National Study Longitudinal of Childhood〞Growing Up in Ireland is funded by the Department of Children and Youth Affairs in Ireland. DK was funded by a stipend from Trinity College Dublin. Compliance with ethical standards
Ethical approval This study was approved by a specially-convened Research Ethics Committee of The National Study Longitudinal of Childhood〞Growing Up in Ireland. Conflict of interest No financial or nonfinancial benefits have been re- ceived or will be received from any party related directly or indirectly to the subject of this article. References
1. Reilly JJ, Kelly J. Long-term impact of overweight and obe- sity in childhood and adolescence on morbidity and premature mortality in adulthood: systematic review. Int J Obes (Lond). 2011;35:891每8. 2. Allison DB, Downey M, Atkinson RL, Billington CJ, Bray GA, Eckel RH, et al. Obesity as a disease: a white paper on evidence and arguments commissioned by the Council of the Obesity Soci- ety. Obesity (Silver Spring). 2008;16:1161每77. 3. Lloyd LJ, Langley-Evans SC, McMullen S. Childhood obesity and adult cardiovascular disease risk: a systematic review. Int J Obes (Lond). 2010;34:18每28. 4. Lauby-Secretan B, Scoccianti C, Loomis D, Grosse Y, Bianchini F, Straif K, et al. Body fatness and cancer每viewpoint of the IARC Working Group. N Engl J Med. 2016;375:794每8. 5. Yajnik CS. The lifecycle effects of nutrition and body size on adult adiposity, diabetes and cardiovascular disease. Obes Rev. 2002;3:217每24. 6. McLeod GF, Fergusson DM, John Horwood L, Carter FA. Adipos- ity and psychosocial outcomes at ages 30 and 35. Soc Psychiatry Psychiatr Epidemiol. 2016;51:309每18. 7. Chaiton M, Sabiston C, O*Loughlin J, McGrath JJ, Maximova K, Lambert M. A structural equation model relating adiposity, psychosocial indicators of body image and depressive symptoms among adolescents. Int J Obes (Lond). 2009;33:588每96. 8. Reinhardt C, Reigstad CS, Backhed F. Intestinal microbiota during infancy and its implications for obesity. J Pediatr Gastroenterol Nutr. 2009;48:249每56. 9. Musso G, Gambino R, Cassader M. Obesity, diabetes, and gut microbiota: the hygiene hypothesis expanded? Diabetes Care. 2010;33:2277每84. 10. Backhed F, Roswall J, Peng Y, Feng Q, Jia H, Kovatcheva-Datch- ary P, et al. Dynamics and stabilization of the human gut microbi- ome during the first year of life. Cell Host Microbe. 2015;17:852. 11. Anglemyer A, Horvath HT, Bero L. Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials. Cochrane Database Syst Rev. 2014;4:Mr000034. 12. Azad MB, Moossavi S, Owora A, Sepehri S. Early-life antibiotic exposure, gut microbiota development, and predisposition to obe- sity. Nestle Nutr Inst Workshop Ser. 2017;88:67每79. 13. Bailey LC, Forrest CB, Zhang P, Richards TM, Livshits A, DeR- usso PA. Association of antibiotics in infancy with early childhood obesity. JAMA Pediatr. 2014;168:1063每9. 14. Trasande L, Blustein J, Liu M, Corwin E, Cox LM, Blaser MJ. Infant antibiotic exposures and early-life body mass. Int J Obes (Lond). 2013;37:16每23. 15. Scott FI, Horton DB, Mamtani R, Haynes K, Goldberg DS, Lee DY, et al. Administration of antibiotics to children before age 2 years increases risk for childhood obesity. Gastroenterology. 2016;151:120每9.e5.
16. Saari A, Virta LJ, Sankilampi U, Dunkel L, Saxen H. Antibi- otic exposure in infancy and risk of being overweight in the first 24 months of life. Pediatrics. 2015;135:617每26. 17. Poulsen MN, Pollak J, Bailey-Davis L, Hirsch AG, Glass TA, Schwartz BS. Associations of prenatal and childhood antibiotic use with child body mass index at age 3 years. Obesity (Silver Spring). 2017;25:438每44. 18. Li DK, Chen H, Ferber J, Odouli R. Infection and antibiotic use in infancy and risk of childhood obesity: a longitudinal birth cohort study. Lancet Diabetes Endocrinol. 2017;5:18每25. 19. Dror T, Dickstein Y, Dubourg G, Paul M. Microbiota manipula- tion for weight change. Microb Pathog. 2017;106:146每61. 20. Gerber JS, Bryan M, Ross RK, Daymont C, Parks EP, Localio AR, et al. Antibiotic exposure during the first 6 months of life and weight gain during childhood. JAMA. 2016;315:1258每65. 21. Shao X, Ding X, Wang B, Li L, An X, Yao Q, et al. Antibiotic exposure in early life increases risk of childhood obesity: a sys- tematic review and meta-analysis. Front Endocrinol (Lausanne). 2017;8:170. 22. Partap U, Allcock SH, Parker E, Gurdasani D, Young EH, Sandhu MS. Association between early life antibiotic use and childhood overweight and obesity: a narrative review. Glob Health Epide- miol Genom. 2018;3:e18. 23. Miller SA, Wu RKS, Oremus M. The association between anti- biotic use in infancy and childhood overweight or obesity: a sys- tematic review and meta-analysis. Obes Rev. 2018;19:1463每75. 24. Rasmussen SH, Shrestha S, Bjerregaard LG, Angquist LH, Baker JL, Jess T, et al. Antibiotic exposure in early life and childhood overweight and obesity: a systematic review and meta-analysis. Diabetes Obes Metab. 2018;20:1508每14. 25. Quail A, Williams J, McCrory C, Murray A, Thornton M. Sample design and response in wave 1 of the Infant Cohort (at 9 months) of Growing Up In Ireland. Dublin: Office of the Minister for Chil- dren and Youth Affairs, Department of Health and Children; 2011. 26. Pan H, Cole TJ. LMSgrowth, a Microsoft Excel add-into access growth references based on the LMS method. Version 2.77. 11017 ed 2012. http://www.healthforchildren.co/uk/. Accessed 1 Nov 2015. 27. Kelleher CC, Viljoen K, Khalil H, Somerville R, O*Brien J, Shrivastava A, et al. Longitudinal follow-up of the relationship between dietary intake and growth and development in the life- ways cross-generation cohort study 2001每2013. Proc Nutr Soc. 2014;73:118每31. 28. Wrieden WL, Longbottom PJ, Adamson AJ, Ogston SA, Payne A, Haleem MA, et al. Estimation of typical food portion sizes for children of diff ent ages in Great Britain. Br J Nutr. 2008;99:1344每53.
29. Food Standards Agency. McCance and Widdowson*s the composi- tion of foods. 7th ed. London: Royal Society of Chemistry; 2014. 30. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed- effects models using lme4. arXiv preprint arXiv:1406.5823; 2014. 31. Bolker B, Su Y-S. Coefplot2: coefficient plots. R package version 0132; 2011. 32. Schwartz BS, Pollak J, Bailey-Davis L, Hirsch AG, Cosgrove SE, Nau C, et al. Antibiotic use and childhood body mass index trajec- tory. Int J Obes (Lond). 2015;40:615每21. 33. Ajslev TA, Andersen CS, Gamborg M, Sørensen TIA, Jess T. Childhood overweight after establishment of the gut microbiota: the role of delivery mode, pre-pregnancy weight and early admin- istration of antibiotics. Int J Obes (Lond). 2011;35:522每9. 34. Murphy R, Stewart AW, Braithwaite I, Beasley R, Hancox RJ, Mitchell EA. Antibiotic treatment during infancy and increased body mass index in boys: an international cross-sectional study. Int J Obes (Lond). 2014;38:1115每9. 35. Langdon A, Crook N, Dantas G. The effects of antibiotics on the microbiome throughout development and alternative approaches for therapeutic modulation. Genome Med. 2016;8:39. 36. Collado MC, Isolauri E, Laitinen K, Salminen S. Distinct com- position of gut microbiota during pregnancy in overweight and normal-weight women. Am J Clin Nutr. 2008;88:894每9. 37. Collado MC, Isolauri E, Laitinen K, Salminen S. Effect of mother*s weight on infant*s microbiota acquisition, compo- sition, and activity during early infancy: a prospective fol- low-up study initiated in early pregnancy. Am J Clin Nutr. 2010;92:1023每30. 38. Linabery AM, Nahhas RW, Johnson W, Choh AC, Towne B, Odegaard AO, et al. Stronger influence of maternal than paternal obesity on infant and early childhood body mass index: the Fels Longitudinal Study. Pediatr Obes. 2013;8:159每69. 39. Cattaneo A, Monasta L, Stamatakis E, Lioret S, Castetbon K, Frenken F, et al. Overweight and obesity in infants and pre-school children in the European Union: a review of existing data. Obes Rev. 2010;11:389每98. 40. Roy SM, Chesi A, Mentch F, Xiao R, Chiavacci R, Mitchell JA, et al. Body mass index (BMI) trajectories in infancy diff by population ancestry and may presage disparities in early childhood obesity. J Clin Endocrinol Metab. 2015;100:1551每60. 41. Berkey CS, Colditz GA. Adiposity in adolescents: change in actual BMI works better than change in BMI z score for longitudinal studies. Ann Epidemiol. 2007;17:44每50. Publisher*s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
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