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Bhramar Mukherjee, PhDProfessor of Biostatistics and Epidemiology
University of Michigan School of Public [email protected]
SAMSI-SAVI Workshop, Mumbai, 2016Working Group # 6
Elena Colicino Sudha Ramalingam
Working Group 6: Epigenomics
Bhramar Mukherjee
The Patriotic Peacocks
Bhramar
Tanujit
Rajani
Prakash
Mohan
Dimple
Sharayu
What is interaction?
Why measure it?
-biology, sub-group identification, improving power
How to measure it?
-Choice of scale, method of analysis, coding
When to report it?
-public health relevance, biological significance, statistical significance
Introduction
Interactions
“Interaction as statisticians think of it is a Weasel
parameter.” –Professor David Clayton, JSM 2012
Weasel Word: “an informal term for words and phrases aimed at creating an impression that a specific and/or meaningful statement has been made, when only a vague or ambiguous claim has been communicated, enabling the specific meaning to be denied if the statement is challenged” (wikipedia)
Statistical Interaction
Very few replicable interactions reported in human observational studies!
Me, 1978
Me, 2016
Gene x Environment x Time
Lead exposure among children in
India: determinants, neurobehavioral
effects and genetic susceptibility
Working Group 6: Data Example
Environmental Health Perspective, 2011
Dataset
Neurotoxicology, 2013
Dataset
World blood lead levels among
children
Burden of disease, 2010
Lead levels and lead in gasoline
USA, NHANES II
( Annest et al. 1983)
Sources of lead exposure
Leaded gasoline phased later than in US
Leaded paint
Occupational:• Garage workers
• Smelting and metal working operations
• Jewelery workers
• Industrial activity
• Mining
Cultural practices • Ayurvedic medication
• Cosmetics (surma, sindhur)
• Holi colors
• Spices
Cosmetics
Religious
powders
Ayurvedic medication
Lead in paint (2009)
Clark, C.S. et al, Lead levels in new enamel household paints from Asia, Africa and
South America. Environ. Res. (2009), doi:10.1016/j.envres.2009.07.002.
Lead Paint
New York Times 2007NDTV 2010
Electronic waste
10-20,000 tonnes, employing 25,000 people, in New Delhi alone
E waste management and handling Rule 2011 ( new law MOEF, India) Needs implementation
Toxics link 2010
Determinants of blood lead levels among 3-7 year old children in Chennai, India (2005-2006)
India Lead Study (Chennai)
Study population (N= 756)• Cross-sectional• 12 schools (3 in 4 zones)• 3-7 year old children
High
industry
Low
Industry
High
traffic
HT/HI
(3 schools)
HT/LI
(3 schools)
Low
traffic
LT/HI
(3 schools)
LT/LI
(3 schools)
Chennai
• Blood lead levels assessed by• LeadCare™ Analyzer
1 . 5 4 . 5 7 . 5 1 0 . 5 1 3 . 5 1 6 . 5 1 9 . 5 2 2 . 5 2 5 . 5 2 8 . 5 3 1 . 5 3 4 . 5 3 7 . 5 4 0 . 5
0
5
1 0
1 5
2 0
2 5
3 0
P
e
r
c
e
n
t
BL LDistribution of blood lead levels (g/dl) in children in
Chennai
N=756
Mean=11.5 g/dl
Range=2.6-40.5 g/dl
55% > 10 µg/dl
2% > 10 µg/dl (NHANES III)
Assessment of Predictors
Questionnaires (primary care givers : Tamil)
• Socioeconomic statuso Family income, parental education, occupationo Type of house
• Possible sources of exposureo Residence (traffic and industry zone), parental occupation, presence of
lead based industry, traffic level near houseo Type of painto Sources and storage of drinking watero Surma and ayurvedic medication use
Predictors of blood lead
Variables Estimate 95% CI Partial R2 **
Age (months) 0.002 -0.001 0.005 0.003
Sex -0.028 -0.094 0.039 0.001
Average monthly family income (Rs)***
<2000 0.259 0.125 0.394 0.028
2000-4000 0.233 0.123 0.342 0.033
4000-6500 0.182 0.081 0.282 0.017
Drinking water storage vessel***
Brass/ Bronze 0.210 0.061 0.359 0.010
Residence ***
High industry 0.074 -0.082 0.231 0.007
* accounting for clustering at school level using generalized estimating equations
** unadjusted for clustering using linear regression
*** compared to >6500 Rs/ month, ** all other drinking water storage vessels, ***low industry area
Total model R2= 5.8%
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
<2000
2000-4000
4000-6500
>6500
Bra
ss/B
ronze
Oth
er
Income (Rs) DWV
Od
ds r
atio
(>10µg/dl)
DWV: Type of vessel used for storage of drinking water.
Adjusted for age (months), sex
p-values<0.05
Conclusions
• Blood lead levelso Lower socioeconomic statuso Drinking water stored in brass or bronze vessels
Residence in a high industry zone (<5 year old)
• No effect of use of ayurvedic medication, surma, traffic, paint
• Little variation in blood lead was explainedo Need in depth exposure assessment
Predictors of blood lead
Lead exposure and behavior,
IQ, Visual Motor skills children
in Chennai, India
Lanphear et al. 2005
Lead and IQ
o IQ is best characterized
• (Needleman 1979, Bellinger 1983)
o No threshold
o Non-linear dose-response
• (Schwartz 1994)
Heated debate!!
Lanphear et al 2005
Behavioral and cognitive assessmentBehavior: Questionnaires administered to the class teachers
Connors ADHD DSM IV Scales (CADS)o ADHD Indexo DSM IV: Hyperactivityo DSM IV: Inattention
Behavior Rating Inventory of Executive Function (BRIEF)o Executive function compositeo Behavioral regulation (inhibit, shift, emotional control)o Metacognition (Initiate, working memory, planning, organization of materials,
monitoring)
Connors Teacher Rating Scales (CTRS-39)o Anxiety, Sociability, (Aggression, Hyperactivity, Inattention)
Behavioral and cognitive assessment (con’t)Intelligence
• Binet - Kamat Intelligence scales ( Tamil)o mental age/ chronological age= IQo administered to children
Genotyping
• Bioserve Hyderabad, India• Mass Array Iplex (Sequenom process)
o PCR and mass spectrometryo Blood
• Negative and positive controls o 24 DNA samples from the Coriell Discovery panel
Effect of lead and hemoglobin (Hb) on IQGeneralized estimating equations*
Roy et al pending publication
Lead and Visual motor skills
Pallaniapan & Roy et al 2011
ConclusionsLead and behavior
• Blood lead levels are associated with poorer behavior and visual-motor skillso ADHD, internalizing behaviors and executive function
• Executive function is most sensitive to lead (0.4 SD)
o 4 IQ points (0.25 SD IQ)o In ADHD, inattention is most affectedo No effect seen on hyperactivity
• Dose-response relationships are linear for behavior
• Blood lead levels are associated with poorer
Lead exposure, iron and
intelligence: genetic
susceptibility
Lead and IQ
Wide variation in effect estimates
• Residual confounding
• Measurement error
• Different dose ranges
• Effect modification
• Nutritional differences
• Genetic differences
Lanphear et al. 2005
Effect modification by
Transferrin C2 polymorphsim
Effect modification by
Transferrin C2 polymorphsim
Roy et al Pending publication
Distribution of DRD2 Taq IA genotype
Effect of lead and Hb on IQ by DRD2 genotype
Roy et al
2011
Hemoglobin, Lead & IQ: genetic susceptibility
-
--
--
-* * * *
IQIQ
Data consists of 159 variables, including genotype data on 18 genetic polymorphisms
We will try to reproduce the published analysis with one marker at a time:
-Choice of confounders
-Transformation of Y and X
-Dose response relationship
-Interpreting interaction on the transformed scale
-Reporting of findings
-How robust are the conclusions
-Extend to incorporate multiple markers, calculate a polygenic risk score.
-Unexplored Associations (birth order related to IQ?)
Plan for Analysis Working Group
Determinants Blood lead BehaviorADHDExecutive functionInternalizing behavior
CognitionIQ
Dopamine D2 receptor polymorphism
IronHemoglobin
SES
Industrial activity
Brass and bronze vessels
OVERARCHING PARADIGM
Transferrin C2 polymorphism
Research Team
Kalpana BalakrishnanKavitha PalaniapanPadmavathi RamaswamyVenkatesh S.M.Shankar K.M.
BIOSERVERama Modali
AKNOWLEDGEMENTS
David C. BellingerJoel SchwartzRobert WrightAnanya Roy
HSPHSRMC
Funding : NIH (R01 ES007821) , Fogarty grant (R03 TW005914)
University of TorontoHoward Hu
YSPHAdrienne Ettinger
Study Participants!
How do we translate all these findings of reported associations and
interactionsinto Public Health action?
Why Should Francesca care?