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Elena Moltchanova, PhD, University of Canterbury, Christchurch, New Zealand

The effect of early growth and development on life-long health. A case study of the Helsinki Birth Cohort. Elena Moltchanova, PhD, University of Canterbury, Christchurch, New Zealand Eero Kajantie , MD, PhD, National Institute for Health and Welfare, Helsinki, Finland. Barker’s Hypothesis.

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Elena Moltchanova, PhD, University of Canterbury, Christchurch, New Zealand

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  1. The effect of early growth and development on life-long health. A case study of the Helsinki Birth Cohort. Elena Moltchanova, PhD, University of Canterbury, Christchurch, New Zealand EeroKajantie, MD, PhD, National Institute for Health and Welfare, Helsinki, Finland

  2. Barker’s Hypothesis Barker, D. J. P., (1992). Fetal and infant origins of adult disease. British Medical Journal: SMALL SIZE AT BIRTH IS CORRELATES WITH HEART DISEASE RISK LATER IN LIFE Developmental Origins of Health and Disease (DOHaD)

  3. Prenatal stress Diabetes ... Prenatal stress vs. life-long health outcomes Low birth weight CVD Obesity Reduced fetal growth

  4. The Helsinki Birth Cohort • 13345 men and women born in Helsinki between 1934-1944 • Data include: gestational age and birth metrics, growth measurements, SES... • ICD codes: • CHD: ICD9: 410-414; ICD10: I21-I25 • Cerebrovascular disease: ICD9: 430-438; ICD10: I60-I69 • Type 1 and Type 2 diabetes... • Hypertension... • Obesity..

  5. Observations (1a)

  6. Observations (1b)

  7. Why summarize? • More parsimonious models (i.e., smaller number of parameters) • Clear interpretation? • The measurements are available at different time points for different individuals • The observations are clearly not independent

  8. A Biologically-Oriented Mathematical Model (ICP) for Human Growth • J. Karlberg (1989) Acta Pediatr Suppl 350:70-94 • Infancy-Childhood-Puberty (ICP) Model • Infancy: Y|t = a + b(1-e-ct) • Childhood: Y|t>t* = ac+ bct + cct2

  9. Infancy + Childhood Component

  10. Infancy Component Y|t = a+b(1-e-ct) Y|t=0 = a Y|t=∞ = (a+b) dY/dt|t=0 = bc dY/dt|t=∞ =0

  11. Observations

  12. IDEFIX: Heights and Weights

  13. Data Selection Process (1) • 13345 ID’s in the initial data set • 199 outlying (4sd) heights • 255 outlying (4sd) weights • 6758 ID’s for which at least 3 valid non-missing observations exist for both, height and weight • 6095 with non-decreasing BMI function at 0 and reasonable values estimated for parameter curves

  14. Data Selection Process (2) • 40 ≤ a.h ≤ 65 • 0 ≤ b.h ≤ 80 • .01 ≤c.h ≤4.99

  15. Fitted Curves (1a)

  16. Fitted Curves (1b)

  17. Fitted Curves (2) 4191 BMI curves with maximum between 0 and 2 years of age

  18. Fitting Summary Statistics

  19. Missing Curves

  20. Missing At Random (MAR) ?Diabetes p = 0.6416

  21. Individual fit of I+C components: Height

  22. Individual fit of I+C components: Weight

  23. Challenges to ponder • Data not Missing-At-Random? • The cohort is too young to detect the differences • ”Generalizability”: will the finding for this cohort be applicable to the modern day situation? • Adaptation • Other Human Growth Models? For example, Reed’s: Y=a+bt+c ln(t+1)+d/(t+1) • ...

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