This document summarizes the results of a study on the impact of maternal nutritional interventions on child health outcomes. It finds that interventions like food and micronutrient supplementation can positively impact birth weight and fetal growth. While evidence on effects on gestational age is limited, supplementation is shown to reduce the risk of low birth weight and small-for-gestational-age babies. The study also finds long term effects on child growth, cognition, and risk of metabolic syndrome from maternal supplementation. Further research is still needed to better understand impacts of preconception interventions and the importance of a life-course approach.
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1. Impact of maternal nutritional
interventions on short and long
term health, survival, and function
Parul Christian, DrPH, MSc
Johns Hopkins Bloomberg School of
Public Health, Baltimore, USA
COREWorkshop Baltimore, Apr 24, 2013
2. Short-term Outcomes
Birth outcomes
◦ Birth weight/size; fetal growth
◦ Gestational age
◦ Stillbirth and perinatal mortality
Neonatal and infant morbidity and mortality
3. Birth weight, FGR and preterm birth
Birth weight is a cumulative measure of
intrauterine growth and gestational age
◦ Low birth weight defined as <2500 g
◦ Birth weight is one of the leading factors
influencing subsequent health and survival in low
income countries where 90% of the 250 million
low birth weight babies are born each year
Underlying causes of LBW are
◦ FGR - Fetal growth restriction (small-for-gestational
age, SGA defined as weight < 10th percentile of
standard for a given GA)
◦ Preterm birth (GA< 37 wk)
4. 4
Systematic literature review &
Meta-analysis
- Included 5 LMIC studies
- 126,176 pregnant women
Apparent dose response
LIMITATIONS
-No indication of etiology of anemia
(iron deficiency, malaria, HIV, etc)
-Timing of anemia in pregnancy
Moderate-Severe Anemia
RR of SGA 1.53 (1.24-1.87)
5. LBW and preterm among adolescents
Gibbs et al, Pediatr Perinatal Epi 2012
6. Preg
Non-
preg
Baseline
1y follow-up
(∆)
Baseline
1y follow-up
(∆)
Between group
difference in ∆, p-value
Height (cm) MUAC (cm)
Mean ± SD
149.2 ± 5.4
149.2 ± 5.3
-0.05 ± 0.72
149.4 ± 5.1
149.7 ± 5.0
0.29 ± 0.82a
<0.001
a. Baseline and follow-up measurements being significantly different with p<0.001
using a paired t-test
<0.001
23.4 ± 1.8
22.7 ± 1.8
-0.65 ± 1.11a
23.2 ± 2.0
23.5 ± 2.0
0.28 ± 0.90a
<0.001
BMI (kg/m2)
19.3 ± 1.7
19.0 ± 1.7
-0.35 ± 1.17a
19.0 ± 2.0
19.3 ± 2.0
0.29 ± 0.98a
Rah et al; J Nutr 2008
Influence of early pregnancy on growth and adolescent
nutritional status in rural Bangladesh
7. Subramanian et al; PlosOne 2011
Height of Nations: Patterns among women in 54 LMIC
8. Maternal supplementation in
pregnancy to reduce SGA and LBW
Balanced energy and protein (food) (Imdad &
Bhutta, Pediatr Peri Epi 2012)
◦ 74 g overall increase in birth weight; 100 g in
malnourished women
◦ 44% reduction in SGA
Iron w/wo folic acid (Imdad & Bhutta, Pediatr Peri Epi
2012)
◦ 20% reduction in LBW
9. Prevalence of micronutrients deficiencies in
early pregnancy in rural Nepal
61.1
39.8
0.7
11.1
28.3
40.3
31.8
37.4
32.8
13.9
40.2
0
20
40
60
%
(Jiang et al; J Nutr 2005)
12. Long-term Outcomes
Linear and ponderal growth in childhood
Long term survival
Cardiometabolic health
◦ Metabolic syndrome
Cognition and motor function
13. Developmental Origins of Health
and Disease - DOHaD
Previously known as the “Barker’s” or
“Early/Fetal Origins” Hypothesis
Early life nutritional and environmental factors
may impact later life disease risk
Most of the focus has been on the association
between size at birth and the risk of
cardiovascular disease and type 2 diabetes in
adulthood
14. Risk of CHD by birth weight
Gluckman and Hanson; 2005
15. Odds ratios for impaired glucose
tolerance or Type II diabetes among
64 yr old men in Hertfordshire
(adjusted for adult BMI)
Odds ratios for metabolic syndrome
among men in Hertfordshire
(adjusted for adult BMI)
Hales & Barker, 2001
16. DOHaD Concepts
Thrifty Genotype (Neel, 1962)
Thrifty Phenotype (Hales & Barker, 1992)
Developmental plasticity
Programming or Developmental Induction (Nathanielsz
1999)
Predictive adaptive response (Gluckman & Hanson, 2005)
Not just “fetal” but postnatal environment is
important
Birth weight is an inadequate marker of prenatal
etiologic pathways
22. Antenatal MMN supplementation
effects on children’s weight and size
at 2 years of age in Nepal
IFA (n=453)
Mean (SD)
MMN (n=462)
Mean (SD)
Difference (95% CI) p-value
WAZ -1.76 (0.98) -1.63 (1.08) 0.14 (0.001, 0.27) 0.048
HAZ -2.28 (1.06) -2.20 (1.12) 0.08 (-0.06, 0.22) 0.048
WHZ -0.40 (1.05) -0.28 (1.12) 0.12 (-0.02, 0.26) 0.097
HC (cm) 46.40 (1.43) 46.64 (1.49) 0.24 (0.06, 0.43) <0.05
BP (mmHg) 101.9 (17.4) 99.4 (13.7) -2.5 (-0.5, -4.6) <0.05
Vaidya et al; Lancet 2008
23. Nepal Study and Interventions(1999-2001)
A double-masked, controlled, cluster randomized
trial of antenatal and postnatal micronutrient
supplementation to examine impact on birth
outcomes and infant survival
5 supplement groups:
◦ C Vitamin A (Control)
◦ FA VA + Folic acid
◦ FAFe Folic Acid and Iron
◦ FAFeZn Folic acid, Iron and Zinc
◦ MM Multiple micronutrient
A cross-sectional follow-up was conducted in
2006-2008 to examine growth, survival, and
biomarkers of cardiometabolic risk in the
offspring at 6-8 y of age
24. Christian et al;AJE 2009
Impact of antenatal micronutrient supplementation
on child survival through 7 y of age: Nepal
25. Anthropometry of children at
birth and at follow-up
Measure Birth 6-8 y old
Mean (SD)
Weight (kg) 2.64 (0.42) 18.05 (2.33)
Length / height (cm) 47.37 (2.26) 113.49 (5.50)
Weight for age z-score -1.52 (1.04) -2.09 (0.89)
Length for age z-score -1.19 (1.11) -1.90 (0.88)
Weight for length z-score -1.01 (1.11) --
BMI for age z-score -1.49 (1.11) -1.22 (0.86)
*Z-scores calculated using WHO growth standard for children <5 y (WHO 2006) and school-aged
children (de Onis 2007)
26. Effect of maternal supplementation on child
anthropometry at 6-8 y of age
Control FA FAFe FAFeZn MM
n=701 n=630 n=641 n=663 n=721
Mean (SD) Difference (95%CI)2
Height (cm) 113.3 (5.4) 0.3 (-0.3,0.9) -0.0 (-0.6,0.6) 0.6 (0.0, 1.3)* -0.1 (-0.7,0.5)
Weight (kg) 18.0 (2.2) 0.0 (-0.3, 0.3) -0.0 (-0.3, 0.3) 0.1 (-0.2, 0.4) -0.1 (-0.4, 0.2)
BMI (kg/m2) 14.0 (1.1) -0.0 (-0.2, 0.1) -0.0 (-0.2, 0.1) -0.1 (-0.2, 0.0) -0.1 (-0.2, 0.1)
Waist circ. (cm) 51.2 (3.0) -0.0 (-0.4, 0.4) 0.0 (-0.4, 0.4) -0.1 (-0.5, 0.3) -0.1 (-0.5, 0.3)
MUAC (cm) 15.4 (1.1) 0.0 (-0.1, 0.2) -0.0 (-0.2, 0.1) -0.0 (-0.2, 0.1) 0.0 (-0.1, 0.2)
Difference from control, adjusted for the age of the child at follow-up and the design effect using a GEE linear regression model. Height
and weight models additionally adjusted for birth length and birth weight, respectively.* p<0.05, difference relative to the control.
Stewart et al; AJCN 2009
27. Differences in triceps and subscapular skinfolds and
arm fat area among children 6-8 y by treatment
-.4
-.2
0
.2
Armfatareadifference(cm2)
-.4
-.2
0
.2
Skinfoldthicknessdifference(mm)
TSF SSF AFA.
Folic acid
Folic acid-iron
Folic acid-iron-zinc
Multiple micronutrient
Maternal supplement group
-0.25 mm (-0.44, -0.06) -0.20 mm (-0.33, -0.06) -0.18 cm2 (-0.34, -0.01)
Stewart et al; AJCN 2009
28. 0
20
40
60
80
100
Meanbloodpressure(mmHg)
Control FA FAFe FAFeZn MM
4
4.5
5
5.5
MeanHbA1c(%)
Control FA FAFe FAFeZn MM0
.1
.2
.3
.4
.5
MedianHOMA-IR
Control FA FAFe FAFeZn MM
HbA1c
Blood Pressure
Insulin resistance (HOMA)
▬▬ Systolic
▬ Diastolic
29. The risk of metabolic syndrome by
maternal supplement group
Control FA FAFe FAFeZn MM
n (%) 75 (11.7) 47 (8.1) 74 (12.2) 70 (11.4) 80 (11.9)
OR
(95% CI)1 1.00
0.63*
(0.41,0.97)
1.02
(0.70,1.49)
0.95
(0.65,1.40)
1.00
(0.69,1.45)
1 Adjusted for child age at follow-up, and the design effect and for fasting status
Stewart et al; J Nutr 2009
30. 1.2
0.4
0.6
0.8
1.0
Oddsratio
FA FAFe FAFeZn MM
The risk of microalbuminuria (MA/CR≥30
mg/g) by maternal supplement group
The risk of microalbuminuria (microalbumin/creatinine ratio ≥30 mg/g. Odds ratios and 95% CI calculated adjusting
for the design effect and child age at follow-up using a GEE logistic model.
0.56 (0.33, 0.93)
0.77 (0.49, 1.22)
0.53 (0.32, 0.89)
0.70 (0.44, 1.11)
Stewart et al; J Nutr 2009
32. 1Using multivariate regression with boot strapping to estimate 95% confidence interval adjusted for design effect;
2Bonferroni adjusted p-values to adjust for multiple comparisons;
3Using multivariate regression with boot strapping to estimate 95% confidence interval adjusted for design effect
and adjusted for child age, sex, ever sent to school, asset score, milk and dairy intake, meat, chicken and fish intake,
lower respiratory infection, diarrhea/dysentery in the past week
4 P-value for the overall treatment effect usingWilks’ lamda and Lawley-Hotelling trace test derived from the
MANOVA with Bonferroni correction applied to the p-values
Differences in test scores in the maternal iron-
folic acid group relative to control
Iron-folic acid
Adj diff (95% CI)3
p-
value3
UNIT 2.38 (0.06, 4.70) 0.04
Failure Stroop test -0.14 (-0.23, -0.04) 0.005
Backward digit test 0.36 (0.01, 0.71) 0.02
% correct no_go -0.54 (-7.44, 6.35) 0.88
MABC -1.47 (-3.06, 0.12) 0.07
Finger tapping test 2.05 (0.87, 3.24) 0.001
P-value4 0.002
Christian et al; JAMA 2010
33. Discussion
Nutritional interventions during pregnancy such as
food and micronutrient supplementation have been
shown to impact fetal growth although evidence for an
effect on gestational duration is limited
Evidence of benefit of preconceptional and early
pregnancy interventions is limited – future research is
urgently needed
The need for a life-course approach for intervening is
reflected in the emphasis on the first 1000 days, but
should be expanded perhaps to -365 days
34. Discussion
In LMICs increasing rates of overweight and
obesity among pregnant women and associated
risks of pregnancy complications and adverse
birth outcomes are of concern
In countries undergoing rapid nutrition transition,
the impact of nutritional advice and counseling
for appropriate weight gain, activity levels and
other life style factors, and adequate nutrient
intakes during pregnancy need further evaluation
Long term cohort follow-ups are needed to
evaluate the impact of early life interventions on
long term cognitive function and cardiometabolic
health