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The association between an unhealthy childhood diet and body composition depends on prenatal experience

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This presentation, from Dr Sarah Crozier, focused on research exploring how the association between an unhealthy childhood diet and body composition depends on prenatal experience. The developmental mismatch hypothesis proposes that risk of diseases such as obesity is increased when impaired prenatal nutrition and growth, is followed by an unhealthy childhood diet. This project used data from the Southampton Women’s Survey (SWS) to investigate whether there was an interaction between conditional growth in fetal abdominal circumference (AC) in late pregnancy and diet at age 6 years on body composition at age 9 years.

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The association between an unhealthy childhood diet and body composition depends on prenatal experience

  1. 1. The association between an unhealthy childhood diet and body composition depends on prenatal experience Sarah Crozier, Janis Baird, Hazel Inskip, Nick Harvey, Sian Robinson, Cyrus Cooper, Mark Hanson and Keith Godfrey Medical Research Council Lifecourse Epidemiology Unit, Southampton, UK
  2. 2. Introduction • The prevalence of obesity is rising in children and is associated with both childhood ill health and an increased risk of subsequent adult obesity • Intrauterine life may be a critical period for the programming of later obesity • The developmental mismatch hypothesis proposes that risk of diseases such as obesity is increased when impaired prenatal nutrition and growth, is followed by an unhealthy childhood diet.
  3. 3. Southampton Women’s Survey 12,583 non-pregnant Southampton women aged 20-34, interviewed about diet, physical activity, social circumstances and lifestyle. 3,158 live-born singleton births. Offspring followed through pregnancy, infancy and beyond.
  4. 4. Abdominal circumference Abdominal circumference was measured at 11 weeks, 19 weeks, 34 weeks, birth, 6 months, 1 year, 2 years, 3 years, 6-7 years and 9 years.
  5. 5. AC size z-scores 0 20 40 60 80 Abdominalcircumference(cm) 0 2 4 6 8 10 Age from birth (years, adjusted for gestation) Lines are mean,2 SDs and 3 SDs
  6. 6. Food frequency questionnaire • The broad pattern of 6 year diet has been characterised by the use of a prudent diet score.
  7. 7. Principal component analysis Weighting Frequency (per week) Total White bread Crisps -0.20 0.3 = -0.1 -0.21 × 0.5 = -0.1 Total = 3.2 × Green vegetables Salad vegetables 0.33 7 = 2.3 0.25 4.5 = 1.1 × ×
  8. 8. Median frequency per week food intake by quarters of the 6 year prudent diet score Food Least prudent quarter Most prudent quarter Salad vegetables 0.8 6 Green vegetables 2.3 6.5 Root vegetables 2 4.3 Other vegetables 0.5 2 Crisps 5 2 Processed meat 7 4.8 White bread 7 1 Chips and roast potatoes 3 1.5
  9. 9. Outcomes • Dual-energy X-ray Absorptiometry (DXA) was used to assess body composition at 9 years; fat, lean and bone mass were derived using paediatric software. • 592 children included in the analysis.
  10. 10. Directed Acyclic Graphs • An analysis can stand or fall on the choice of confounders • A Directed Acyclic Graph (DAG) or causal diagram describes a model of the associations between all variables that could influence the exposure- outcome association.
  11. 11. DAG • Adjust for: 9 year height, sex, breastfeeding duration, maternal BMI, education, smoking in pregnancy, late pregnancy vitamin D and pregnancy weight gain Maternal education Pre- pregnancy BMI 9 year height Late pregnancy vitamin D Sex 9 year body compositio0n AC growth Smoking in pregnancy Age at DXA Pregnancy weight gain Duration of breastfeeding
  12. 12. Characteristics Characteristic Maternal education ≥ A-levels, n (%) 376 (63.6%) Pre-conception BMI, kg/m2 [median (IQR)] 24.0 (22.1 to 27.0) Female, n (%) 303 (48.8%) Age at DXA scan, years [mean (SD)] 9.2 (0.3) 9 year total fat, kg [median (IQR)] 7.6 (5.7, 10.0) 9 year total lean, kg [mean (SD)] 22.7 (3.3) 9 year total BMC, kg [mean (SD)] 1.0 (0.1) n = 592
  13. 13. Main effects - fat mass n = 199 -.2 0 .2 .4 .6 .8 Regressioncoefficient(95%CI)11 w eek size 11-19 w eek grow th 19-34 w eek grow th 34 w eek-birth grow thBirth-6 m onth grow th6-12 m onth grow th 12 m onth-2 yeargrow th 2-3 yeargrow th 3-6 yeargrow th 9 year total fat (SDs)
  14. 14. Main effects – percentage fat n = 199 -.2 0 .2 .4 .6 .8 Regressioncoefficient(95%CI)11 w eek size 11-19 w eek grow th 19-34 w eek grow th 34 w eek-birth grow thBirth-6 m onth grow th6-12 m onth grow th 12 m onth-2 yeargrow th 2-3 yeargrow th 3-6 yeargrow th 9 year percentage fat (SDs)
  15. 15. Main effects – total lean n = 199 -.2 0 .2 .4 .6 .8 Regressioncoefficient(95%CI)11 w eek size 11-19 w eek grow th 19-34 w eek grow th 34 w eek-birth grow thBirth-6 m onth grow th6-12 m onth grow th 12 m onth-2 yeargrow th 2-3 yeargrow th 3-6 yeargrow th 9 year total lean (SDs)
  16. 16. Main effects – total BMC n = 199 -.2 0 .2 .4 .6 .8 Regressioncoefficient(95%CI)11 w eek size 11-19 w eek grow th 19-34 w eek grow th 34 w eek-birth grow thBirth-6 m onth grow th6-12 m onth grow th 12 m onth-2 yeargrow th 2-3 yeargrow th 3-6 yeargrow th 9 year total BMC (SDs)
  17. 17. Interaction – total fat P-value for interaction = 0.006 -.4 -.2 0 .2 .4 9yeartotalfat(SDs) -0.7 -0.2 0.2 0.7 Conditional AC growth 34 weeks to birth (SDs) -0.7 -0.2 0.2 0.7 Prudent diet score (SDs)
  18. 18. Interaction – percentage fat P-value for interaction = 0.005 -.4 -.2 0 .2 .49yearpercentagefat(SDs) -0.7 -0.2 0.2 0.7 Conditional AC growth 34 weeks to birth (SDs) -0.7 -0.2 0.2 0.7 Prudent diet score (SDs)
  19. 19. Interaction – total lean P-value for interaction = 0.97 -.2 -.1 0 .1 .2 .3 9yeartotallean(SDs) -0.7 -0.2 0.2 0.7 Conditional AC growth 34 weeks to birth (SDs) -0.7 -0.2 0.2 0.7 Prudent diet score (SDs)
  20. 20. Interaction – total BMC P-value for interaction = 0.94 -.3 -.2 -.1 0 .1 .2 .3 9yeartotalBMC(SDs) -0.7 -0.2 0.2 0.7 Conditional AC growth 34 weeks to birth (SDs) -0.7 -0.2 0.2 0.7 Prudent diet score (SDs)
  21. 21. Strengths and weaknesses • Strengths Detailed anthropometric measurement and conditional growth analyses enabled description of abdominal circumference growth. Dietary patterns describe broad patterns of diet, with greater potential for public health intervention. Directed acyclic graphs provide an objective method to determine confounders, aiming to describe causality. • Weakness The conditional growth method only provides measures of fetal growth for participants with abdominal circumference data at all time points.
  22. 22. Conclusions • Individuals showing late gestation faltering of fetal growth who then had an unhealthy childhood diet had greater adiposity, while childhood diet was less influential on adiposity in individuals whose fetal growth had not faltered. • There were no similar interactions for lean and BMC outcomes. • The result for adiposity provides some evidence in support of the mismatch hypothesis.
  23. 23. Acknowledgements Funding: • Medical Research Council • University of Southampton • Dunhill Medical Trust • British Heart Foundation Ultrasonographers Doctors Administrative staff Nurses and Midwives Telephonists 12,583 SWS study participants Dieticians/nutritionists Research assistants Clerical staff Laboratory staff Statisticians Computing staff • Food Standards Agency • National Institute for Health Research • European Union’s Seventh Framework Programme

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