1 | 1.1 Topic goes here | Project number | 14.12.08 Copyright © 2008 National University Health System
Education
Clinical Care
Research
Early interventions to prevent NCDs: leads from the GUSTO study Yap-Seng CHONG Associate Professor, Yong Loo Lin School of Medicine, National University of Singapore Acting Executive Director, Singapore Institute for Clinical Sciences, A*STAR 17 October 2014
Scope of presentation
• Overview of GUSTO
• Interpretation of the neonatal methylome
• Gestational diabetes
• Maternal emotional health
• Late preterm and near term deliveries
• Consequentialist epidemiology - engaging
stake holders
Growing Up in Singapore Towards healthy Outcomes (GUSTO)
o Life course cohort of 1247 mother-child dyads of 3 Asian ethnicities
launched on 1 June 2009; children now between 3.5 to 5 years old.
o Funded initially by $25 million NRF TCR Flagship Programme Grant,
with substantial additional funding from SICS and Industry; TCR
Flagship Programme Grant renewed this year.
Hypotheses: • Genomic, environmental and developmental (from fetal to early childhood)
factors can influence risk of subsequent non-communicable diseases
(NCDs) in individuals.
• Hypothesis 1: Specific epigenetic marks can be detected in conceptual
tissues at birth, which are a reflection of different developmental
environments.
• Hypothesis 2: These epigenetic marks, in consort with phenotypic and
genomic measures and nutritional and related exposures in early life, predict
patterns of development in infancy, which lead to NCDs.
Life Course
Cohort Design
• 2 maternity units
– NUH and KKH
• Total subjects
– 1200 mothers
• Pregnant women recruited before 14 weeks gestation
and followed up till delivery
• Babies followed up through home and clinic visits
• Recruitment:
– 1 June 09 – 1 July 09 (Vanguard): 100 subjects
– 17 August 09 (Main Cohort): 1100 subjects
– Completed recruitment 9 September 2010 (after 15 mths)
– Last baby born on 1 May 2011
Fetal & Maternal Tracking in Pregnancy
1247 mothers recruited In 1st trimester
Recruitment
completed in 15 months
Deep Phenotyping
of Mothers
at 26 weeks
24/7 on-call team Umbilical cord & placenta
Snap freezing Preparation
Collection of
Specimens at
Delivery
Maternal, Paternal, Cord blood, etc
Regular 3 monthly visits for 18 months Yearly clinic visits for 24 - 48 months
Over 8,500
visits made
so far
Father Demographic and lifestyle data
Physical health
Anthropometric measurements
Biospecimens collection
Mother Physical and mental health
Diet and nutrition
Physical examination
Anthropometric measurements
Biospecimens collection
Child Physical health
Cognitive function
Diet and nutrition
Medications & supplement use
Physical examination
Body composition measurements
Biospecimens collection
Day 1 – Body Composition
Bioelectrical
impedance
Air displacement
plethysmography
Skinfold
measurement
MRI
Day
BIA
Preparation
Immobilization
bag
Reconsent
Skinfold &
Anthropometry
Day 7-10
Postnatal
386 MRIs
done without
sedation
For a
subset, MRI
is repeated
at:
Week 6
Month 6
MR Imaging
Whole Body Study: Head & Chest, Abdomen & Lower Limbs
Adipose tissue growth by water-suppressed MRI during first 6 months of life (to scale)
Age on the day of MRI and weight of the infant are also shown.
MRI at day 9; 2.73kg MRI at day 56; 4.74kg MRI at day 200; 7.25kg
Longitudinal Assessment of Abdominal Adipose
Tissue Deposition in first 6 months of life
FRACTIONAL ANISOTROPY: INTEGRITY OF
AXONS, IMPORTANT IN
MEDIATING NEUROLOGICAL
FUNCTIONS
3-Monthly Home Visits for 1st 15 months
Appointment
Visiting
homes
Buccal swabs Questionnaires
Clinic Visits at 6, 18, 24, 36, 48 and 54 months: Neurodevelopmental and other detailed assessments
Computerised
Eye Tracking Electrophysiology
Behavioural Observation Nearly3,000 clinic visits so far
Interrogating the BioSamples Over 120,000 samples Blood chemistry
Including micronutrients, metabolomics
Genotyping
Omniexpress+ exome arrays
SNP and CNV
Methylome assessment
Infinium 450K arrays
RRBS
Methyl-capture-seq
Chromatin and histone assessment
TaCH / DNAase protection
Native ChIP-seq
Mnase-seq
Transcriptome assessment
Infinium HT12 v4 arrays
RNAseq
miRNAseq
Metabolome Analyses
GC-MS and LC-MS
Microbiome assessment
16S RNA sequencing
Metagenomics & metatranscriptomics
Umbilical cord and
placenta
Maternal and fetal blood
Longitudinal buccal swabs
Cord derived MSCs
Hair sample
Longitudinal microbiota
sampling
Genomics Epigenomics Transcriptomics Proteomics Metabolomics
Demographics & Social Determinants
Nutrition & Metabolism
Maternal Emotional Well-Being
Body Composition (e.g. MRI) & Growth (including fetal ultrasounds)
Cardiovascular changes (including retinal vessels changes)
Oral Health
Child Health & Allergy
Neurodevelopment
Endocrine changes
Interaction with environment e.g. the Exposome Microbiome Metagenomics &
Metatrascriptomics
, , & & NIPPer, , & & NIPPer
Maternal Health e.g. gestational diabetes mellitus
Deep Phenotyping
Pre-conception Pregnancy Postnatal and
non-gravid
Early childhood and later life
Longitudinal measures throughout the Life Course
What have we found so far?
Epigenetics
Proportions of 1423 VMRs over 237 neonate methylomes best explained by Genotype only or Gene x Environment models
(none were best explained by environment only)
G x E
(74.6%)
Genotype
alone
(25.4%)
Proportions explained by different in utero environments interacting with genotype
GxE Birthweight
GxE Maternal smoking
GxE Parity
GxE Maternal Age
GxE Maternal Depression
GxE Gestational Age
G&E maternal BMI
Genotype only
Examples of VMRs whose methylation are significantly associated with phenotype in only one genotypic group
Teh AL, Pan H, Chen L, Ong ML, Dogra S, Wong J, Macisaac JL, Mah SM, McEwen LM, Saw SM, Godfrey KM, Chong YS, Kwek K, Kwoh CK, Soh SE, Chong MF, Barton S, Karnani N, Cheong CY, Buschdorf JP, Stunkel
W, Kobor MS, Meaney MJ, Gluckman PD, Holbrook JD. (2014). The effect of genotype and in utero environment on inter-individual variation in neonate DNA methylomes. Genome Research. April 7.
“This research provides important new
evidence that fixed changes in a
baby's genes have only a modest
influence on its epigenetic profile at
birth and that most of the variation
between babies arises from
interactions between the environment
experienced in the womb and the
genetic information inherited from the
parents."
Implications
• Most of the variation between newborns arises from interactions between the intrauterine environment and the genetic information inherited from the parents.
• Analyses of environmental influence on the methylome that fail to consider the moderating effect of genotype may produce misleading estimates of the impact of specific environmental conditions.
Gestational Diabetes
OGTT at 26-28 weeks
Prevalence of detected GDM in GUSTO cohort using universal testing versus high risk screening
•75 gram OGTT at 26-28 weeks gestation
•WHO criteria: 7.0 mmol/L fasting plasma glucose and/or 7.8 mmol/L 2h post OGTT
Increased risk of Diabetes Mellitus for offspring: •Amongst offspring of women with GDM, 21 % had diabetes or prediabetes in contrast to only 4% in offspring of background population. [Damm P, IJGO 2009;104:S25-S26.]
Increased risk of Overweight and Obesity for offspring: •Doubled in offspring of women with diet controlled GDM compared to background population.
Increased risk of future diabetes: •Women with GDM have a 19% risk of developing Type 2 DM 9 years after the pregnancy. [Feig DS, CMAJ 2008;179(3):229-34.]
Whole cohort (n=1136) Chinese (n=644) Malays (n=290) Indians (n=202) Universal testing 215 (18.9%) 135 (21.0%) 35 (12.1%) 45 (22.3%)
High Risk screening 111 (9.8%)** 45 (7.0%)** 21 (7.2%)** 45 (22.3%)
**P<0.001 compared to Universal Screening.
GDM
Major
Driver
of
NCDs
Aris IM et al. Effect of maternal glycemia on neonatal adiposity in a multiethnic asian birth cohort.
J Clin Endocrinol Metab. 2014 Jan;99(1):240-7.
Even “normal” glucose levels are associated with
increased birthweight and adiposity (birthweight >90th centile, % body fat >90th centile, sum of skinfolds >90th centile)
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
1 2 3 4 5 6
Fre
qu
ency
(%
) >
90th
per
cen
tile
Glucose category
Fasting plasma glucose
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
18.0%
20.0%
1 2 3 4 5 6
Fre
qu
ency
(%
) >
90th
per
cen
tile
Glucose category
2-hour plasma glucose (OGTT 2-hr)
Birthweight
% body fat
sum of skinfolds
(<4.2) (≥5.3 mmol/L) (<5.1) (≥8.8mmol/L)
Significant linear
trend for fasting glucose for all 3 adiposity outcomes (p<0.0005) but not for 2h glucose
Journal of Clinical Endocrinology & Metabolism 2014 Jan;99(1):240-7.
Nutrition and GDM
Non GDM
(n=762)1
GDM (n=163)2
p-value
Mean ± SEM Mean ± SEM
Energy (kcal) 1912.36 ± 21.24 1845.43 ± 37.87 0.124
Protein (% of energy) 15.41 ± 0.14 16.45 ± 0.32 0.002
Fat (% of energy) 32.53 ± 0.27 31.77 ± 0.64 0.255
Carbohydrate (% of energy) 52.07 ± 0.31 51.78 ± 0.76 0.708
Plasma folate (nmol/L) 37.14 ± 1.27 43.23 ± 2.72 0.044
Plasma vitamin B12 (pmol/L) 222.50 ± 2.86 208.55 ± 5.99 0.040
Effects Of Maternal Dietary Protein, Folate And Vitamin B12
Status On Glucose Intolerance During Pregnancy Chong M.F.F et al.
Greater GDM prevalence is associated with
•Higher dietary protein (OR=1.35 per SD of percent protein, 95% CI=1.09-1.67)
•Higher plasma folate (OR=1.96 per SD of log-folate, 95% CI=1.17-3.31)
•Lower plasma vitamin B12 (OR=0.80 per SD of vitamin B12, 95%CI=0.64-0.98)
Iron status and prevalence of GDM Pang W.W et al.
Prevalence of GDM
Iron intake (quintiles) Unadjusted Odds Ratio (95% CI) *Adjusted Odds Ratio (95% CI)
Q1-Q4 Referent Referent
Q5 3.00 (1.73, 5.14) 3.34 (1.76, 6.35)
p<0.001 p<0.001
Iron intake (quintiles) Unadjusted B (95% CI) *Adjusted B (95% CI)
Q1-Q4 Referent Referent
Q5 0.02 (-0.10, 0.14) -0.02 (-0.14, 0.10)
p=0.747 p=0.701
Fasting plasma glucose (mmol/L)
Iron intake (quintiles) Unadjusted B (95% CI) *Adjusted B (95% CI)
Q1-Q4 Referent Referent
Q5 0.57 (0.24, 0.90) 0.51 (0.19, 0.83)
p=0.001 p=0.002
OGTT 2hr (mmol/L)
Elevated Iron Intake associated with higher GDM and 2H-post load plasma glucose
Micronutrient status in GUSTO mothers at
26-28 weeks of gestation
Plasma micronutrients
Deficient n (%)
Insufficient n (%)
Sufficient n (%)
Folate 110 (11.0) 25 (2.5)
863 (86.5)
Vitamin B12 161 (16.1)
407 (40.8)
430 (43.1)
Vitamin B6 164 (16.5)
0 (0.0)
828 (83.5)
Iron 42 (8.4) 0 (0.0)
458 (91.6)
Gestation Weight Gain (GWG),
Pre-Pregnancy BMI, and
Gestational Diabetes (GDM)
Excessive Early GWG
Johnson et al, Obstet Gynecol 2013, 121:969-75. •N= 8,923, 16 centers in the Eunice Kennedy Shriver National Institute of Child and
Human Development Maternal-Fetal Medicine Units Network, USA, 2003 to 2008.
•About 3 of every 4 women (73%) gained more weight than was
recommended by the 2009 IOM guidelines.
Carreno et al, Obstet Gynecol 2012, 119:1227–33. •In an analyses of 7,985 women (44% White Caucasian, 24% African Americans, 30%
Hispanics and 2% other races):
•48% had excessive early GWG (15-18 weeks).
•93% of women with excessive early GWG had total weight gain
during the pregnancy that was greater than IOM guideline.
•GDM, LGA, and birth weight >4,000g were higher in women with
excessive early GWG.
Pre-Pregnancy BMI • Overweight and obese women are nearly two times more likely to
exceed the IOM recommended gains compared to normal weight
women.
• The national nutrition survey in China from 1992 to 2002:
– The prevalence of overweight (BMI ≥ 24 kg/m2) women 18–44 years of age
increased from 16.8 to 21.8 %
– The prevalence of obesity (BMI ≥ 28 kg/m2) increased from 3.1 to 6.1 %
• In the USA, between 2004 and 2005:
– 23% of women began pregnancy as overweight (BMI 25.0–29.9 kg/m2)
– 19% began pregnancy as obese (BMI ≥ 30.0 kg/m2)
• 2012 Health Survey in England:
– 32% of women of childbearing age were overweight (BMI 25.0–29.9 kg/m2)
– 18% starting pregnancy as obese (BMI ≥ 30.0 kg/m2)
– 20–40% of women gained more than the recommended weight during
pregnancy, resulting in an increased risk of maternal and fetal complications
• Chu, S.Y., S.Y. Kim, and C.L. Bish, Matern Child Health J, 2009. 13(5): p. 614-20 • Thangaratinam, S., et al., Health Technol Assess, 2012. 16(31): p. iii-iv, 1-191
• Ma GS, Li YP, Wu YF et al .Zhonghua Yu Fang Yi Xue Za Zhi, 2005, 39:311–315
Pre-pregnancy BMI of GUSTO mothers
• Using the WHO BMI (IOM) cut-off, there will be
significantly less overweight or obese subjects
(23.6%) than if the Asian standards (35.5%) were
used (p<0.001).
Asian BMI WHO BMI (IOM)
Catergories PrePregnancy BMI n= % PrePregnancy BMI n= %
underweight < 18.5 81 12.6 < 18.5 81 12.6
normal 18.5 - 22.9 335 51.9 18.5 - 24.9 412 63.9
overweight 23.0 - 27.4 147 22.8 25.0 - 29.9 110 17.1
obese ≥ 27.5 82 12.7 ≥ 30.0 42 6.5
Distribution of GWG Relative to IOM (2009) Guidelines by Pre-pregnancy BMI P
rop
ort
ion
(%
) o
f w
om
en
> IOM
Within IOM
< IOM
Pre-pregnancy BMI (kg/m2)
Underweight < 18.5
Normal weight (18.5-24.9)
Overweight (25.0-29.9)
Obese (≥ 30.0)
N Min Max Mean SD TGWG 1104 -7 31 11.20 4.529
Pro
po
rtio
n (
%)
of
wo
men
> IOM
Within IOM
< IOM
Pre-pregnancy BMI (kg/m2)
Underweight < 18.5
Normal weight (18.5-22.9)
Overweight (23.0-27.4)
Obese (≥ 27.5)
N Min Max Mean SD TGWG 1104 -7 31 11.20 4.529
Distribution of GWG Relative to IOM (2009) Guidelines by Pre-pregnancy BMI (Asian cut-offs)
BMI categories *Adjusted
Underweight
( <18.5kg/m2)
(n=122)
OR=0.961
(95%CI 0.313-2.948)
P=0.945
Normal weight
( 18.5-24.9kg/m2)
(n=653)
OR=0.832
(95%CI 0.414-1.674)
P=0.607
Overweight
( 25-30kg/m2)
(n=181)
OR=2.927
(95%CI 1.007-8.508)
P=0.048
Obese
(>30kg/m2)
(n=76)
OR=1.296
(95%CI 0.271-6.194)
P=0.745
*Adjusted for previous history of macrosomic births, GDM and family history of diabetes
Effect of Pre-pregnancy BMI on GUSTO GDM risk
BMI categories Insufficient weight gain Excess weight gain
Underweight
( <18.5kg/m2)
(n=122)
OR=2.662
(95%CI 0.296-23.924)
P=0.382
OR=0.823
(95%CI 0.216-2.596)
P=0.739
Normal weight
( 18.5-
24.9kg/m2)
(n=653)
OR=0.973
(95%CI 0.408-2.322)
P=0.951
OR=0.807
(95%CI 0.398-1.634)
P=0.551
Overweight
( 25-30kg/m2)
(n=181)
OR=1.897
(95%CI 0.515-6.990)
P=0.336
OR=3.254
(95%CI 1.096-9.664)
P=0.034
Obese
(>30kg/m2)
(n=76)
OR=0.849
(95%CI 0.135-5.333)
P=0.862
OR=1.476
(95%CI 0.297-7.331)
P=0.634
Effect of early inappropriate weight gain up to
26 weeks of gestation on GUSTO GDM risk
Adjusted for previous history of macrosomic births, GDM and family history of diabetes
Can anything be done?
Can gestational weight gain be modified by increasing physical activity
and diet counseling? A meta-analysis of interventional trials. Streuling I, Beyerlein A, von Kries R. Am J Clin Nutr. 2010 Oct;92(4):678-87.
CONCLUSIONS: Interventions based on physical activity and dietary counseling, usually
combined with supplementary weight monitoring, appear to be successful in reducing GWG.
Health messages about GWG • If you start with abnormal BMI, you will tend to have GWG outside
recommended range:
– Aim for normal BMI pre-pregnancy.
• If you have insufficient or excessive GWG, you will have higher risk
of abnormal perinatal, neonatal, and maternal outcomes:
– Gain within the ranges recommended:
• ~ 0.5–2 kg weight gain in the first trimester (up to Week 12)
• ~ 0.45 kg/week (0.36-0.59) for pre-pregnancy BMI ≤24.9
• ~ 0.23-0.27 kg/week (0.18-0.32) for pre-pregnancy BMI ≥25.0
• Implementation:
– Record pre-pregnancy height and weight.
– Chart women’s weight gain throughout pregnancy.
– Share the results with them so they are aware of their progress
toward their gestational weight gain goal.
– Watch out for excessive early GWG.
– Watch out for women with BMI>23.
– Diet and exercise.
Health messages about GWG
“Eating for Two” ≠ Doubling what you Eat
“Eating for Two” = Thinking Twice about
•How much you Eat
•How much you Exercise
•How Much Weight you Gain in Pregnancy
Possible interventions to prevent
GDM consequences • Before Pregnancy
– Aim for normal BMI
– Aim for micronutrient balance
• During Pregnancy
– Prudent nutrition
– Watch early GWG
– Chart and maintain normal GWG
– Universal testing for GDM
• After pregnancy
– Aim for normal BMI again
– Follow up GDM mothers and their children
• Combination of diet and physical activity
• Combination of surveillance, guidelines, education, and self-efficacy
Maternal Stress
and
Early Neurodevelopment
Maternal stress
• Prevalence of antenatal depression can be as high as 20%, while 12-16% experience postnatal depression. [Leung et al. J Am Diet Assoc 2009 Sep;109:1566-75.]
• In GUSTO, between 7-11% of mothers experienced depressive symptoms, and over 40% had symptoms of anxiety. [Chong MF et al. J Psychiatr Res 2014 Aug; 55:110-6.]
Figure 1: The red contour indicates the amygdala on diffusion
tensor imaging and T2-weighted magnetic resonance imaging.
Antenatal maternal depression affects the neonatal microstructure of the right amygdala (lower fractional anisotropy
and axial diffusivity in a brain
region closely associated
with vulnerability for mood
anxiety disorders).
Suggest the prenatal
transmission of
vulnerability for depression
from mother to child.
NEUROCOGNITION
Catechol-O-methyltransferase (COMT) Haplotypes Modulate Associations of Antenatal Maternal Anxiety and
Neonatal Cortical Morphology
Anqi Qiu, Ta Anh Tuan, Mei Lyn Ong, Yue Li, Anne Rifkin-Graboi, Helen
Chen, Birit FP Broekman, Kenneth Kwek, Seang-Mei Saw, Yap-Seng Chong, Peter D. Gluckman, Marielle V. Fortier, Joanna Dawn Holbrook, Michael J. Meaney, “COMT Haplotypes Modulate Associations of Antenatal Maternal Anxiety and
Neonatal Cortical Morphology”, Accepted by American Journal of Psychiatry.
COMT SNPs (val158met,
rs737865, and rs165599)
modulated the association
between antenatal maternal
anxiety and the prefrontal
and parietal cortical
thicknesses of neonatal
brains (involved in executive
functioning and processing
of sensory information).
Possible Interventions
• Systematic Surveillance
– All women screened before, during and after pregnancy for Emotional Health status
• Systematic Support
– Women at risk identified and offered support
– Specialized support service for Women’s Emotional Health
• Essential Service
– Not a frill
Gestation at delivery
(at or near term)
Abitbol, C. L. & Rodriguez, M. M. Nat. Rev. Nephrol. 8, 265–274 (2012);
published online 28 February 2012; doi:10.1038/nrneph.2012.38
Late preterm births: a growing problem
• From 1990 to 2006, singleton preterm births increased 14% from 9.7% to 11.1% in the USA. – Singleton preterm births <34 weeks
increased only 1% from 2.93% to 2.96%
– 99% of the increase in singleton preterm births was seen among late (34-36 week) preterm births, which contributed 73% of the problem*
• In Singapore, late preterm births comprised 85.1% of preterm births in the GUSTO cohort.
Martin JA, Hamilton BE, Sutton PD, et al. Births: final data for 2006. National vital
statistics reports, vol. 57, no 7. Hyattsville (MD): National Center for Health Statistics; 2009
Ananth C, Gyamfi C, Jain L. Characterizing risk profiles of
infants who are delivered at late preterm gestations: does it
matter? Am J Obstet Gynecol 2005;199:330.
So what?
Transcriptome changes affecting hedgehog and cytokine signaling in the umbilical cord: implications for disease risk Walter Stünkel, Hong Pan, Siew Boom Chew, Emilia Tng, Jun Hao Tan, Li Chen, Roy Joseph, Clara Y Cheong, Mei-Lyn Ong, Yung Seng Lee, Yap-Seng Chong,
Seang Mei Saw, Michael J Meaney, Kenneth Kwek, Allan M Sheppard, Peter D Gluckman, GUSTO Study Group & Joanna D Holbrook
PLoS One July 7th 2012
-1.50 1.50
≤ 37w_NBW > 37w_NBW
Set of genes whose expression levels are high in samples from babies with GA ≤ 37 weeks FDR p <0.05
Set of genes whose expression levels are low in samples from babies with GA ≤ 37 weeks, FDR p<0.05
Umbilical cords from babies born at less than 37 weeks gestation
Umbilical cords from babies born at more than 37 weeks gestation
Clear difference in the transcriptome of umbilical cords at different (but not extreme) gestational ages
Z-score RNA exp
Possible Interventions
• Educate Obstetricians about the long term consequences of their management
– Look beyond the APGAR score
• Review current guidelines regarding gestational age for deliveries
– E.g. diet-controlled GDM at 40 weeks
• Reinforce policy to do elective deliveries after 39 weeks gestation
– Censure social inductions and caesareans before 39 weeks gestation
What leads for early intervention has
GUSTO contributed so far? • Discovery
1. 20% gestational diabetes rate instead of 8% • Current screening policy misses at least 50% of mothers with GDM
2. Antenatal maternal depression affects offspring brain structure and
infant neurodevelopment
3. Babies born even a little early may suffer metabolic and neurologic
consequences
4. At least 16% Singaporean women deficient in certain micronutrients • Micronutrient deficiencies have effects on mother and offspring
5. Genotype and environment jointly influence epigenetic modification
• Change in Practice
1. Universal testing for gestational diabetes
2. Emotional health support for mothers
3. Elective deliveries should be scheduled later
4. Preconception micronutrient intervention
• Breakthrough Science
5. New approaches to neonatal methylome analyses
Consequentialist Epidemiology: Engaging Stakeholders
59
April 30, 2014 May 5, 2014
Grant Reviewer: “This program has all the characteristics of a world class, cutting edge translational
and clinical research flagship program of which the investigators (and Singapore NMRC) can be
very proud. It has immense potential for continuing research that may well change many aspects
of pregnancy management and infant and childhood health practices in the future.”
SUMMARY
• GUSTO unveiled unexpected problems and new findings close to
home that suggest possible interventions and new research
questions.
• Interventions to prevent long-term bad outcomes are a hard sell
but probably should:
• Have short-term measurable outcomes e.g. prevent an
undesired current maternal condition
• Target locally important conditions
• Be relatively easy to implement
• Capture the heart and head of the public and policy makers
• Be enforceable
• Do no harm
• Active engagement of industry, public and policy makers required.
PI, Co-Is and Collaborators
62
Allan Sheppard, UoA,NZ
Amutha Chinnadurai, NUHS
Anne Eng Neo Goh, KKH
Anne Rifkin-Graboi, SICS
Anqi Qiu, NUS
Arijit Biswas, NUHS
Audrey Chia, SNEC
Bee Wah Lee, NUHS
Bernard Chern, KKH
Birit FP Broekman, SICS, NUHS
Boon Long Quah, SNEC
Borys Shuter
Carolina Un Lam, NUHS
Chai Kiat Chng, KKH
Chan Yiong Huak, NUS
Cheryl Ngo, NUHS
Choon Looi Bong, KKH
Christiani Jeyakumar Henry, SICS
Chuen Seng Tan, NUS
Chun Ming Ding, SICS
Citra Mattar, NUHS
Claudia Chi, NUHS
Cornelia Yin Ing Chee, NUHS
Doris Fok, NUHS
E Shyong Tai, NUHS
Eric Andrew Finkelstein, Duke-NUS
Elizabeth Spelke, Harvard, US
Evelyn Xiu Ling Loo, NUHS
Fabian Yap, KKH
Fook Tim Chew, NUS
George Seow Heong Yeo, KKH
Goh Yam Thiam Daniel, NUHS
Hazel Inskip, USoton, UK
Helen Y. H Chen, KKH
Helen Zhou Juan, Duke-NUS
Heng Hao Tan, KKH
Hugo P S van Bever,NUHS
Iliana Magiati, NUS
Inez Bik Yun Wong, NUHS
Ivy Yee-Man Lau, SMU
Jeevesh Kapur, NUHS
Jenny L. Richmond, UNSW,Au
Jerry Kok Yen Chan, KKH, NUS
Joanna D. Holbrook, SICS
Joanne Yoong, NUS
Jonathan Choo, KKH
Joshua J. Gooley, Duke-NUS
Keith M. Godfrey, USoton, UK
Kenneth Kwek, KKH
Kok Hian Tan, KKH
Krishnamoorthy Naiduvaje, NUHS
Leher Singh, NUS
Lieng Hsi Ling, NUHS
Lin Lin Su, NUHS
Lourdes Mary Daniel, KKH
Lynette Pei-Chi Shek, NUHS
Marielle Fortier, NUHS
Mark Hanson, USoton, UK
Mary Foong-Fong Chong, SICS
Mary Rauff, NUHS
Mei Chien Chua, KKH
Melvin Leow, SICS
Michael Heymann, Auckland, NZ
Michael Meaney, SICS
Mya Thway Tint, NUHS
Neerja Karnani, SICS
Ngee Lek, KKH
Oon Hoe Teoh, KKH
P. C. Wong, NUHS
Paulin Tay Straughan, NUS
Peter D. Gluckman, SICS
Philip Baker, Auckland, NZ
Pratibha Agarwal, KKH
Queenie Ling Jun Li, SERI
Rob M. van Dam, NUS
Robert Grignani, NUHS
Salome A. Rebello, NUS
Seang-Mei Saw, NUS
See Ling Loy, KKH
Sendhil Velan, SICS
Seng Bin Ang, KKH
Shang Chee Chong, NUHS
Shirong Cai, NUHS
Shu-E Soh, NUHS
Sok Bee Lim, KKH
Srilatha Balasubramanian, NUHS
Stephen Chin-Ying Hsu, NUHS
Swee Chye Quek, NUHS
Teng Hong Tan, KKH
Tong Wei Yew, NUHS
Victor Samuel Rajadurai, KKH
Walter Stunkel, SICS
Wayne Cutfield, Auckland, NZ
Wee Meng Han, KKH
Wei Wei Pang, NUHS
Wen Chin Chiang, KKH
Yap Seng Chong, NUHS
Yen Ling Low, NUS
Yin Bun Cheung, Duke-NUS
Yung Seng Lee, NUHS
Zhongwei Huang, NUHS
, and
"Does our civilisation have a future?“ ……Richard Horton, Editor-in-Chief, The Lancet, United Kingdom “We are failing - as individuals, societies and a species - to collectively organise ourselves in ways that assure our long term survival. How can innovation create global institutions that save us from ourselves?” .….Kelley Lee, Director of Global Health, Simon Fraser University, Canada “What is the appropriate relationship between state and market in ensuring human well-being and security?” ……Steven Weber, Political Science & I-School, University of California, USA
Program
Guest-of-Honour: Mr Heng Swee Keat, Minister for Education, Singapore
•Planetary Health •Technology for Equity •Building Global Institutions for the Future: Transition and Innovation •International Relations, Political Economy and Geopolitical Perspectives •Round Table Panel Discussion
Send enquiries to: [email protected]
Thank you