The document discusses building a search engine to identify environmental factors associated with phenotypes and diseases. It notes that while genetics research has advanced through techniques like genome-wide association studies, understanding of environmental influences lacks comparable methods. The author argues that a data-driven "exposomic" approach is needed to discover environmental factors, leverage existing exposure data, consider total evidence, and apply machine learning to observational data. This could help explain currently unknown sources of phenotypic variation and disease risk.
Data analytics to support exposome research course slidesChirag Patel
We present new publicly available tools to bootstrap your own data-driven investigations to correlate the environment with phenotype. Course materials here: http://www.chiragjpgroup.org/exposome-analytics-course/
Real-Time Genome Sequencing of Resistant Bacteria Provides Precision Infectio...ExternalEvents
http://www.fao.org/about/meetings/wgs-on-food-safety-management/en/
Real-Time Genome Sequencing of Resistant Bacteria Provides Precision Infection Control in an Institutional Setting. Presentation from the Technical Meeting on the impact of Whole Genome Sequencing (WGS) on food safety management and GMI-9, 23-25 May 2016, Rome, Italy.
dkNET Webinar: Population-Based Approaches to Investigate Endocrine Communica...dkNET
Abstract
Mechanisms of inter-organ signaling have been established as hallmarks of nearly every pathophysiologic condition, where many exist as related and complex diseases. While significant work has been focused on understanding how individual cell types contribute and respond to specific perturbations related to common, complex disease, an equally-important but relatively less-explored question involves how relationships between organs are altered in the context of an integrated living organism. Current technical advances, such as proteomic analysis of plasma or conditioned media, have allowed for a more unbiased visualization and discovery of additional inter-tissue signaling molecules. However, one important feature which is lacking from these approaches is the ability to gain insight as to the function, mechanisms of action and target tissue(s) of relevant molecules. To begin to address these constraints, we initially developed a correlation-based bioinformatics framework which uses multi-tissue gene expression and/or proteomic data, as well as publicly available resources to statistically rank and functionally annotate endocrine proteins involved in tissue cross-talk. Using this approach, we identified many known and experimentally validated several novel inter-tissue circuits. This was this first study to directly link an endocrine-focused bioinformatics pipeline from population data directly to experimentally-validated mechanisms of inter-tissue communication. While these validations provide strong support for exploiting natural variation to discover new modes of communication, these serve as simple proof-of-principle studies and, thus, have promising potential for expansion. Some of these will be discussed during the presentation.
Presenter: Marcus Seldin, Ph.D. Assistant Professor, Biological Chemistry, University of California Irvine
Upcoming webinars schedule: https://dknet.org/about/webinar
Identification of PFOA linked metabolic diseases by crossing databasesYoann Pageaud
The increasing amount of biological data makes possible their interpretation more accurate and richer than never before. Various way of representations and interpretations of the links between those data have been applied or developed consequently to these new elements which can be taken into account in diagnostics and soon in personalized medicine. The aim of this student project was to cross data coming from various databases to be able to link Perfluorooctaoic Acid (PFOA) to one or more human phenotypes and metabolic diseases. Our approach makes possible an easy and confident interpretation on the data kept and also allow us to rank diseases linked according to their risk of correlation to a specific set of proteins.
Data Visualization in Biomedical Sciences: More than Meets the EyeNils Gehlenborg
In science, data visualization serves two primary purposes. The first is to explore data sets interactively and the second is to communicate discoveries. However, the requirements for visualizations employed in these activities are very different. Therefore, the software tools used for these purposes are typically disconnected, creating significant challenges for reproducibility and effective communication of discoveries in data-driven biomedical science. In this presentation, I will address how a new approach to creating data visualization tools can connect data analysts and other stakeholders inside and outside the scientific community. I will introduce and demonstrate the "Vistories" approach that was motivated by these question.
Presented at the 5th Cancer Research UK Big Data Analytics Conference on Data Visualization.
Data analytics to support exposome research course slidesChirag Patel
We present new publicly available tools to bootstrap your own data-driven investigations to correlate the environment with phenotype. Course materials here: http://www.chiragjpgroup.org/exposome-analytics-course/
Real-Time Genome Sequencing of Resistant Bacteria Provides Precision Infectio...ExternalEvents
http://www.fao.org/about/meetings/wgs-on-food-safety-management/en/
Real-Time Genome Sequencing of Resistant Bacteria Provides Precision Infection Control in an Institutional Setting. Presentation from the Technical Meeting on the impact of Whole Genome Sequencing (WGS) on food safety management and GMI-9, 23-25 May 2016, Rome, Italy.
dkNET Webinar: Population-Based Approaches to Investigate Endocrine Communica...dkNET
Abstract
Mechanisms of inter-organ signaling have been established as hallmarks of nearly every pathophysiologic condition, where many exist as related and complex diseases. While significant work has been focused on understanding how individual cell types contribute and respond to specific perturbations related to common, complex disease, an equally-important but relatively less-explored question involves how relationships between organs are altered in the context of an integrated living organism. Current technical advances, such as proteomic analysis of plasma or conditioned media, have allowed for a more unbiased visualization and discovery of additional inter-tissue signaling molecules. However, one important feature which is lacking from these approaches is the ability to gain insight as to the function, mechanisms of action and target tissue(s) of relevant molecules. To begin to address these constraints, we initially developed a correlation-based bioinformatics framework which uses multi-tissue gene expression and/or proteomic data, as well as publicly available resources to statistically rank and functionally annotate endocrine proteins involved in tissue cross-talk. Using this approach, we identified many known and experimentally validated several novel inter-tissue circuits. This was this first study to directly link an endocrine-focused bioinformatics pipeline from population data directly to experimentally-validated mechanisms of inter-tissue communication. While these validations provide strong support for exploiting natural variation to discover new modes of communication, these serve as simple proof-of-principle studies and, thus, have promising potential for expansion. Some of these will be discussed during the presentation.
Presenter: Marcus Seldin, Ph.D. Assistant Professor, Biological Chemistry, University of California Irvine
Upcoming webinars schedule: https://dknet.org/about/webinar
Identification of PFOA linked metabolic diseases by crossing databasesYoann Pageaud
The increasing amount of biological data makes possible their interpretation more accurate and richer than never before. Various way of representations and interpretations of the links between those data have been applied or developed consequently to these new elements which can be taken into account in diagnostics and soon in personalized medicine. The aim of this student project was to cross data coming from various databases to be able to link Perfluorooctaoic Acid (PFOA) to one or more human phenotypes and metabolic diseases. Our approach makes possible an easy and confident interpretation on the data kept and also allow us to rank diseases linked according to their risk of correlation to a specific set of proteins.
Data Visualization in Biomedical Sciences: More than Meets the EyeNils Gehlenborg
In science, data visualization serves two primary purposes. The first is to explore data sets interactively and the second is to communicate discoveries. However, the requirements for visualizations employed in these activities are very different. Therefore, the software tools used for these purposes are typically disconnected, creating significant challenges for reproducibility and effective communication of discoveries in data-driven biomedical science. In this presentation, I will address how a new approach to creating data visualization tools can connect data analysts and other stakeholders inside and outside the scientific community. I will introduce and demonstrate the "Vistories" approach that was motivated by these question.
Presented at the 5th Cancer Research UK Big Data Analytics Conference on Data Visualization.
Mel Reichman on Pool Shark’s Cues for More Efficient Drug DiscoveryJean-Claude Bradley
Mel Reichman, senior investigator and director of the LIMR Chemical Genomics Center at the Lankenau Institute for Medical Research presents at the chemistry department at Drexel University on November 12, 2009.
Modern drug discovery by high-throughput screening (HTS) begins with testing hundreds of thousands of compounds in biological assays. The confirmed hit rate for typical HTS is less than 0.5%; therefore, 99.5% of the costs of HTS are for generating null data. Orthogonal convolution of compound libraries (OCL) is 500% more efficient than present HTS practice. The OCL method combines 10 compounds per well. An advantage of this method is that each compound is represented twice in two separately arrayed pools. The potential for the approach to better enable academic centers of excellence to validate medicinally relevant biological targets is discussed.
Presenter: Marina Sirota, UCSF
Recent advances in genome typing and sequencing technologies have enabled quick generation of a vast amount of molecular data at very low cost. The mining and computational analysis of this type of data can help shape new diagnostic and therapeutic strategies in biomedicine. In this talk, I will discuss how such technological advances in combination with data science and integrative analysis can be applied to drug discovery in the context of drug target identification, computational drug repurposing, and population stratification approaches.
Professor Michael Levin's presentation at Meningitis Research Foundation's 2013 conference Meningitis & Septicaemia in Children & Adults www.meningitis.org/conference2013
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Question1
A cross-sectional study was conducted to examine the effect of gestational age on systolic blood pressure (SBP) of low birth weight babies who weigh less than 1500 gms. Data was collected on 60 such babies and posted on Moodle in the Excel file Assign2Q1.xls. The dataset contains the following variables.
ID: Participant ID number
sbp = Systolic blood pressure (mmHg)
gestage = gestational age in weeks
a) What are the study factor and the outcome factor?
b) To explore the association, calculate the correlation coefficient and interpret it?
c) Conduct a simple linear regression using Stata and report the Stata output. What are the assumptions for a linear regression? Examine the assumptions with the support of relevant graphs and statistics.
d) Write down the regression equation and interpret the regression coefficients and their 95% confidence interval from part c.
e) What is the expected systolic blood pressure of a newborn whose gestational age is 24 weeks? Show your workings.
Page 2 of 9
PHCM9498EpidemiologyandStatistics–
Question2
A case-control study was planned to investigate whether there was an association between a mother being diagnosed with toxaemia (A condition in pregnancy, also known as pre-eclampsia characterized by abrupt hypertension, albuminuria and oedema) and the baby being born with low birth weight. The research team wished to recruit the cases and controls from antenatal clinics. Based on a pilot study, the team expected that the odds ratio of the association in question would be 2.5 using a two-sided significance test and the prevalence of toxaemia among women giving birth to a normal weight baby is 6%.
a) If equal number of cases and controls could be recruited in this study, how many in each group would be required to achieve 90% power at 5% level of significance? Include a screenshot of your Stata command and output with your response.
b) One of the researchers thought that prevalence of toxaemia among the controls would be 4%.
i. What effect will this have on the required sample size to detect an OR of 2.5 with the same power and level of significance as in part a)?
ii. If the prevalence of toxaemia in the control group is uncertain, would it be preferable to assume that 4% or 6% of the control mothers have the condition? Describe your reason.
c) A similar study on the same source population found that approximately 80% of the mothers approached for the study would agree to participate. From this information, how many mothers of newborn will need to be approached to achieve the sample size that you est.
Similar to Chirag patel unite for sight 041418 (20)
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
How to Give Better Lectures: Some Tips for Doctors
Chirag patel unite for sight 041418
1. Building a search engine to find and
robustly identify environmental factors
with phenotype and disease
Chirag J Patel
Unite for Sight
4/14/2018
chirag@hms.harvard.edu
@chiragjp
www.chiragjpgroup.org
2. P = G + EType 2 Diabetes
Cancer
Alzheimer’s
Gene expression
Phenotype Genome
Variants
Environment
Infectious agents
Diet + Nutrients
Pollutants
Drugs
3. We are great at G investigation!
>4000 (as of 1/1/18)
36,066 G-P associations
Genome-wide Association Studies (GWAS)
https://www.ebi.ac.uk/gwas/
G
4. Nothing comparable to elucidate E influence!
E: ???
We lack high-throughput methods
and data to discover new E in P…
7. σ2
G
σ2
P
H2 =
Heritability (H2) is the range of phenotypic
variability attributed to genetic variability in a
population
Indicator of the proportion of phenotypic
differences attributed to G.
8. Height is an example of a heritable trait:
Francis Galton shows how its done (1887)
“mid-height of 205 parents
described 60% of variability of 928
offspring”
What else describes height?
18. We just don’t know:
Is everything we are exposed to associated with cancer?
Schoenfeld and Ioannidis, AJCN 2012
50 random ingredients from
Boston Cooking School
Cookbook
Any associated with cancer?
Of 50, 40 studied in cancer risk
Weak statistical evidence:
non-replicated
inconsistent effects
non-standardized
19. … we just don’t know
http://fivethirtyeight.com/features/you-cant-trust-what-you-read-about-nutrition/
21. So the problem remains:
(2) and how do we find the stuff that matters?
E: ???
Diet
Infection
Pollution
Drugs
22. We are great at G investigation!
>4000 (as of 1/1/18)
36,066 G-P associations
Genome-wide Association Studies (GWAS)
https://www.ebi.ac.uk/gwas/
G
How did genetics-based investigations advance?
(And advance so quickly?)
23. A new paradigm of GWAS for discovery of G in P:
Human Genome Project to GWAS
Sequencing of the genome
2001
HapMap project:
http://hapmap.ncbi.nlm.nih.gov/
Characterize common variation
2001-current day
High-throughput variant
assay
< $99 for ~1M variants
Measurement tools
~2003 (ongoing)
Nature 2008
Comprehensive, high-throughput analyses
GWAS
24. How can we do better in both discovery and
translation?:
Leverage data-driven “exposomic” techniques!
• Data-driven discovery
• search through all the possibilities
• gauge the totality of the evidence
• New ways to measure the exposome (E)!
• scalable ways to measure diet, infection,
pollution
25. Explaining the missing variation:
A data-driven paradigm for robust discovery of E in disease
via systematic study of the “exposome”
what to measure? how to measure?
“A more comprehensive view of
environmental exposure is
needed ... to discover major
causes of diseases...”
how to analyze in relation to health?
Wild, 2005, 2012
Rappaport and Smith, 2010, 2011
Buck-Louis and Sundaram 2012
Miller and Jones, 2014
Patel CJ and Ioannidis JPAI, 2014
26. Possible to use existing technologies for E
Exposure (and P) Assessment…
CEBP 2017
… however, heterogeneous measures that require different
study designs and analytic approaches.
27. Promises and Challenges in creating a search engine for
identifying E in P
JAMA 2014
ARPH 2016
JECH 2014
Curr Epidemiol Rep 2017
29. Gold standard for breadth of human exposure information:
National Health and Nutrition Examination Survey1
since the 1960s
now biannual: 1999 onwards
10,000 participants per survey
1 http://www.cdc.gov/nchs/nhanes.htm
>250 exposures (serum + urine)
GWAS chip
>200 quantitative clinical traits
(e.g., serum glucose, lipids, body
mass index)
Death index linkage (cause of
death)
30. Gold standard for breadth of exposure & behavior data:
National Health and Nutrition Examination Survey
Nutrients and Vitamins
vitamin D, carotenes
Infectious Agents
hepatitis, HIV, Staph. aureus
Plastics and consumables
phthalates, bisphenol A
Physical Activity
e.g., stepsPesticides and pollutants
atrazine; cadmium; hydrocarbons
Drugs
statins; aspirin
31. What E are associated with aging:
all-cause mortality, heart disease, and
telomere length?
Int J Epidem 2013
Int J Epidem 2016
32. Identifying E associated with all-cause mortality:
Data-driven searching through 253 associations
age (10 years)
income (quintile 2)
income (quintile 1)
male
black income (quintile 3)
any one smoke in home?
Multivariate cox (age, sex, income, education, race/ethnicity, occupation [in red])
serum and urine cadmium
[1 SD]
past smoker?
current smoker?serum lycopene
[1SD]
physical activity
[low, moderate, high activity]*
*derived from METs per activity and categorized by Health.gov guidelines
R2 ~ 14%
(2%)
34. What about other factors related to aging?:
452 associations in Telomere Length!
Int J Epidem 2016
PCBs
FDR<5%
Trunk Fat
Alk. PhosCRP
Cadmium
Cadmium (urine)cigs per day
retinyl stearate
R2 ~ 1%
VO2 Maxpulse rate
shorter telomeres longer telomeres
adjusted by age, age2, race, poverty, education, occupation
median N=3000; N range: 300-7000
2-8 years
35. Interdependencies of the exposome:
Correlation globes paint a complex view of exposure
Red: positive ρ
Blue: negative ρ
thickness: |ρ|
for each pair of E:
Spearman ρ
(575 factors: 81,937 correlations)
permuted data to produce
“null ρ”
sought replication in > 1
cohort
Pac Symp Biocomput. 2015
JECH. 2015
36. Red: positive ρ
Blue: negative ρ
thickness: |ρ|
for each pair of E:
Spearman ρ
(575 factors: 81,937 correlations)
Interdependencies of the exposome:
Correlation globes paint a complex view of exposure:
average correlation of < 0.3
permuted data to produce
“null ρ”
sought replication in > 1
cohort
Pac Symp Biocomput. 2015
JECH. 2015
Effective number of
variables:
500 (10% decrease)
37. How can we do better in both discovery and translation?:
Leverage data-driven “exposomic” techniques!
• Data-driven discovery
• search through all the possibilities
• gauge the totality of the evidence
• New ways to measure the exposome (E)!
• scalable ways to measure diet, infection,
pollution
38. Data-driven discovery to identifying factors that matter!
1.) Find elusive E in P and
explain variation of disease risk
2.) Consideration of totality of
evidence: Does my correlation
matter?
3.) Machine learning methods to
detecting signals in observational and
large data
39. Data-driven discovery to identifying factors that matter!
1.) Find elusive E in P and explain
variation of disease risk
2.) Consideration of totality of
evidence: Does my correlation
matter?
3.) Machine learning methods to detecting
signals in observational and large data
ARPH 2016
JAMA 2014
JECH 2015
40. Data-driven discovery to identifying factors that matter!
1.) Find elusive E in P and explain
variation of disease risk
2.) Consideration of totality of
evidence: Does my correlation
matter?
3.) Machine learning methods to
detecting signals in observational and
large data ARPH 2016
JAMA 2014
JECH 2015
41. How can we do better in both discovery and translation?:
Leverage data-driven “exposomic” techniques!
• Data-driven discovery
• search through all the possibilities
• gauge the totality of the evidence
• New ways to measure the exposome (E)!
• scalable ways to measure diet, infection,
pollution
42. Explaining the missing variation:
A data-driven paradigm for robust discovery of E in disease
via systematic study of the “exposome”
what to measure? how to measure?
“A more comprehensive view of
environmental exposure is
needed ... to discover major
causes of diseases...”
how to analyze in relation to health?
Wild, 2005, 2012
Rappaport and Smith, 2010, 2011
Buck-Louis and Sundaram 2012
Miller and Jones, 2014
Patel CJ and Ioannidis JPAI, 2014
43. Need to assess the exposome globally:
(e.g., India and China)
c/o Getty Images c/o AFP
44. … and Sub-Saharan Africa!
Can we predict HIV as a function of the exposome?
AIDS 2018
45. Harvard DBMI
Susanne Churchill
Nathan Palmer
Sophia Mamousette
Sunny Alvear
Chirag J Patel
chirag@hms.harvard.edu
@chiragjp
www.chiragjpgroup.org
NIH Common Fund
Big Data to Knowledge
Acknowledgements
RagGroup
Arjun Manrai
Nam Pho
Jake Chung
Kajal Claypool
Chirag Lakhani
Danielle Rasooly
Alan LeGoallec
Sivateja Tangirala
Mentioned Collaborators
Isaac Kohane
John Ioannidis
Dennis Bier
Hugo Aschard