gms | German Medical Science

62. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

17.09. - 21.09.2017, Oldenburg

Early life factors and later cardio-metabolic outcomes in European children

Meeting Abstract

  • Maren Pflüger - Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Deutschland
  • Claudia Börnhorst - Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Deutschland
  • Gabriele Eiben - Section for Epidemiology and Social Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Schweden
  • Licia Iacoviello - Laboratory of Molecular and Nutritional Epidemiology, Department of Epidemiology and Prevention, IRCCS Istituto Neurologico Mediterraneo Neuromed, Pozzilli, Isernia, Italien
  • Lauren Lissner - Section for Epidemiology and Social Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Schweden
  • Staffan Mårild - Section for Epidemiology and Social Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Schweden
  • Denes Molnár - Department of Pediatrics, University of Pécs, Pécs, Ungarn
  • Luis A. Moreno - GENUD (Growth, Exercise, Nutrition and Development) Research Group, Faculty of Health Sciences, Universidad de Zaragoza, Instituto Agroalimentario de Aragón (IA2), Zaragoza, Spanien; Instituto de Investigación Sanitaria Aragón (IIS Aragón), Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBERObn), Zaragoza, Spanien
  • Paola Russo - Epidemiology and Population Genetics, Institute of Food Sciences, National Research Council, Avellino, Italien
  • Michael Tornaritis - Research and Education Institute of Child Health, Strovolos, Zypern
  • Toomas Veidebaum - Department of Chronic Diseases, National Institute for Health Development, Tallinn, Estland
  • Mahmoud Zaqout - Department of Public Health, Ghent University, Ghent, Belgien
  • Wolfgang Ahrens - Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Deutschland; Institute of Statistics, Faculty of Mathematics and Computer Science, University of Bremen, Bremen, Deutschland

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 62. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Oldenburg, 17.-21.09.2017. Düsseldorf: German Medical Science GMS Publishing House; 2017. DocAbstr. 290

doi: 10.3205/17gmds024, urn:nbn:de:0183-17gmds0245

Veröffentlicht: 29. August 2017

© 2017 Pflüger et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe http://creativecommons.org/licenses/by/4.0/.


Gliederung

Text

Introduction: The “fetal origins of adult disease” hypothesis by Barker proposes that exposures during critical periods of growth and development lead to changes of cardio-metabolic markers. Elevated levels of cardio-metabolic markers have been suggested to track into adulthood and influence the manifestation of metabolic syndrome (MetS) and cardiovascular diseases [1], [2], [3]. Prospective studies showed associations between foetal growth and MetS or its components waist circumference (WC) /BMI, blood pressure, blood lipids and blood glucose in children and adolescents [4], [5], [6]. Pregnancy-related factors like excess gestational weight gain, maternal diabetes and overweight were associated with increased risk or higher level for MetS and its components in childhood [7], [8], [9]. Previous results are often inconsistent, based on small study samples and factors for changes of blood glucose and blood lipids have been less often investigated. Therefore, this study investigates the long-term effects of pre-, peri- and postnatal factors on the MetS and cardio-metabolic parameters in a large pan-European cohort of children and adolescents.

Methods: The analysis is based on the IDEFICS/ I.Family cohort. We followed children aged 2.0-9.9 years at baseline with physical examinations, collection of biological samples and questionnaires over three examination waves between 2007 and 2014 (T0, T1, T3) [10]. Data from maternity cards, records of routine child visits, and questionnaires on prenatal and early life factors (maternal smoking during pregnancy, gestational weight gain, maternal age at birth, birth size, premature birth, Caesarean section or spontaneous birth, breastfeeding duration, age at introduction of solid food) of 12,698 children were used. Mixed-effects models were used to analyse the associations between these factors and z-scores of the MetS and cardio-metabolic outcomes (WC, diastolic and systolic blood pressure (DBP/SBP), blood lipids triglycerides and HDL cholesterol, Homeostasis model assessment of insulin resistance (HOMA-IR) and fasting blood glucose).

Results: Obesity was observed in 26.7%, hypertension in 19.1%, insulin resistance in 21.0%, dyslipidaemia in 19.3% and MetS in 6.2% of all children. Analysis showed after Bonferroni adjustment for multiple testing that WC z-score was positively associated with large for gestational age-status (LGA) (ß=0.38, p<0.0001), daily smoking of the mother (ß=0.23, p<0.0001) and gestational weight gain (ß=0.01, p<0.0001) while small for gestational age-status (SGA) and preterm delivery were negatively associated with WC z-score (SGA: ß=-0.45, p<0.0001; preterm delivery: ß=-0.1, p<0.0001). Additionally, SBP z-score was positively associated with SGA (ß=0.14, p<0.0001) and preterm delivery (ß=0.09, p<0.0001) and negatively associated with LGA (ß=-0.09, p<0.0001). Maternal age at birth, Caesarean section, breastfeeding and the age at introduction to solid food did not show any association with cardio-metabolic markers.

Discussion: Our findings provide further evidence that birth size is positively associated with WC and blood pressure in children and adolescents [11], [12]. In contrast to previous studies, we did not observe associations of gestational or early life factors with blood glucose, blood lipids or MetS [13], [14], [15], [16], [17]. Our data do not support the hypothesis that gestational and early life factors are associated with subsequent changes of these cardio-metabolic parameters. Children born preterm, SGA or LGA may warrant closer monitoring to prevent adverse health outcomes later on.



Die Autoren geben an, dass kein Interessenkonflikt besteht.

Die Autoren geben an, dass ein positives Ethikvotum vorliegt.


References

1.
Barker DJ. The fetal and infant origins of disease. Eur J Clin Invest. 1995;25(7):457-63.
2.
Barker DJ, et al. The relation of small head circumference and thinness at birth to death from cardiovascular disease in adult life. BMJ. 1993;306(6875):422-6.
3.
Barker DJ, et al. Growth in utero, blood pressure in childhood and adult life, and mortality from cardiovascular disease. BMJ. 1989;298(6673):564-7.
4.
Huang RC, et al. Perinatal and childhood origins of cardiovascular disease. Int J Obes (Lond). 2007;31(2):236-44.
5.
Huang RC, Mori TA, Beilin LJ. Early life programming of cardiometabolic disease in the Western Australian pregnancy cohort (Raine) study. Clin Exp Pharmacol Physiol. 2012;39(11):973-8.
6.
Theodore RF, et al. Childhood to Early-Midlife Systolic Blood Pressure Trajectories: Early-Life Predictors, Effect Modifiers, and Adult Cardiovascular Outcomes. Hypertension. 2015;66(6):1108-15.
7.
Gaillard R, et al. Childhood cardiometabolic outcomes of maternal obesity during pregnancy: the Generation R Study. Hypertension. 2014;63(4):683-91.
8.
Boney CM, et al. Metabolic syndrome in childhood: association with birth weight, maternal obesity, and gestational diabetes mellitus. Pediatrics. 2005;115(3):e290-6.
9.
Efstathiou SP, et al. Metabolic syndrome in adolescence: can it be predicted from natal and parental profile? The Prediction of Metabolic Syndrome in Adolescence (PREMA) study. Circulation. 2012;125(7):902-10.
10.
Ahrens W, Siani A, Adan R, De Henauw S, Eiben G, Gwozdz W, Hebestreit A, Hunsberger M, Kaprio J, Krogh V, Lissner L, Molnár D, Moreno LA, Page A, Picó C, Reisch L, Smith RM, Tornaritis M, Veidebaum T, Williams G, Pohlabeln H, Pigeot I; I.Family consortium. Cohort Profile: The transition from childhood to adolescence in European children-how I.Family extends the IDEFICS cohort. Int J Epidemiol. 2016 Dec;:. DOI: 10.1093/ije/dyw317 Externer Link
11.
Kuhle S, et al. Birth Weight for Gestational Age, Anthropometric Measures, and Cardiovascular Disease Markers in Children. J Pediatr. 2017;182:99-106.
12.
Renom Espineira A, et al. Postnatal growth and cardiometabolic profile in young adults born large for gestational age. Clin Endocrinol (Oxf). 2011;75(3):335-41.
13.
Nordman H, et al. Growth and Cardiovascular Risk Factors in Prepubertal Children Born Large or Small for Gestational Age. Horm Res Paediatr. 2016;85(1):11-7.
14.
Harville EW, et al. Is the metabolic syndrome a "small baby" syndrome?: the bogalusa heart study. Metab Syndr Relat Disord. 2012;10(6):413-21.
15.
Arends NJ, et al. Reduced insulin sensitivity and the presence of cardiovascular risk factors in short prepubertal children born small for gestational age (SGA). Clin Endocrinol (Oxf). 2005;62(1):44-50.
16.
Huxley R, et al. Birth weight and subsequent cholesterol levels: exploration of the "fetal origins" hypothesis. Jama. 2004;292(22):2755-64.
17.
Reinehr T, Kleber M, Toschke AM. Small for gestational age status is associated with metabolic syndrome in overweight children. Eur J Endocrinol. 2009;160(4):579-84.