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: rafflesdialogue@nuhs.edu.sg

Thank you