Gene and Environment in the Development of Allergic Diseases · 2014-10-22 · Comparison of wheeze and atopy between Seoul and Jeongup area Lee So-Yeon, et al. Int Arch Allergy Immunol

Gene and Environment in the Development of Allergic Diseases

Childhood Asthma Atopy Center Research Center for Standardization of Allergic Diseases Dept of Pediatrics, Asan Medical Center, Ulsan University,

Seoul, Korea

Hong Soo-Jong MD,PhD

대알춘계학술대회 2012.5.26

Data from 1995, 2000 and 2005 Korean ISAAC study Data from 2008 Atopy Free Seoul study * P < 0.05

Increase of Prevalence of Childhood AD in Seoul from 1995 to 2008

0

10

20

30

40

50

1995 2000 2005 2008

AD symptom, ever

AD symptom, last 12 months

AD diagnosis, ever

AD treatment, last 12 months

year

*

* *

* *

*

%

13.7% 13.6% 16.0% 17.5%

19.7%

27.5% 29.2%

29.4%

* *

• Most common chronic diseases in Korean children : AR, AD

(Korea Health and Wealth Administration, NHNS 2002)

• Increase of AD since last 20 yrs in worldwide and Korean children from ISAAC study

Lee Jung Yong, et al. Int Arch Allergy Immunol 2011

• Genetic predisposition

• Increase exposure and sensitization to

allergen

• Change of Life style : Obesity, Diet pattern,

Nutrition, Physical activity

• Hygiene hypothesis

• Climate change, Air pollution, ETS

Why increasing allergic diseases ?

Reason of Increase of Allergic Disease : Hygiene Hypothesis

• Hygiene hypothesis :

– 1989 by Strachan

– Increase of number of older sibling decrease of allergic disease : due to unhygienic condition and more infection

• Hygiene hypothesis : Life style changes in developed country decrease of infection or improve hygiene condition increase of allergic disease

• Microbial hypothesis :

- Wold AE. 1998. Revised to intestinal flora ?

Ever wheeze (AVQ)

%

Atopy rate (SPT)

Korean Model of Hygiene Hypothesis : Comparison of wheeze and atopy between Seoul and Jeongup area

Lee So-Yeon, et al. Int Arch Allergy Immunol 2012

Seoul Village Village Town Town Seoul

Farming environment and rural lifestyle might be associated with protective factors impacting the prevalence of allergic diseases and atopy.

Multivariate logistic regression analysis of potential risk factors (adjusted odds ratio, 95% CI) for

allergic rhinitis diagnosis ever

Lee So-Yeon , et al. Int Arch Allergy Immunol 2012

Hygiene Hypothesis Data from Korea

0

0.2

0.4

0.6

0.8

1

1.2

Parent-

agriculture

Farm animal

exp at preg

Farm animal at

home

aOR

Prevalence of Allergic Ds in Rural area

reference

asthma Dx

AR Dx

AD Dx

* P< 0.05

*

*

* *

Lee So-Yeon , et al. Int Arch Allergy Immunol 2012;158:168-74

Hygiene Hypothesis Data from Korea

0

0.2

0.4

0.6

0.8

1

1.2

Asthma

diagnosis

AR

diagnosis

AD

diagnosis

Atopy

aOR

Diagnosis of Allergic Ds Regarding to Older Sibling

older sibling 0

older sibling 1

older sibling 2

P< 0.05 P< 0.05 P< 0.05

Seo Ju-Hee, et al. Annals Allergy Asthma Immunol 2012 (In submission)

Exposure to Environmental Microorganisms and Childhood Asthma

Ege MJ, et al. NEJM 2011

Relationship between Microbial Exposure and the Probability of Asthma

Ege MJ, et al. NEJM 2011

The risk of asthma decreased significantly with the increase in the number of detectable bands in the PARSIFAL study and with the number of fungal taxa in GABRIELA.

Intestinal Microbiota Hypothesis :

Multiple lifestyle factors may affect intestinal microbiota in early life

→ affect the development of immune tolerance in the intestinal mucosa

→ influence the development of immune system finally

→ may affect the development of immune mediated diseases

Wold AE. The hygiene hypothesis revised: is the rising frequency of allergy due to changes in intestinal flora? Allergy 1998;53:20-5

Microbiome of Various Anatomical Location of the Human Body

Lee YK, et al. Science 2010

• Human Microbiome Project since 2007 over 150 times than human genome • 1000-1150 bacterial species 160 bacteria species/each person

Lifestyle changes have caused a fundamental alteration in association with the microbial world.

How the Microbiome and the Human Genome Contribute to Inflammatory Disease

The community composition of the human microbiome helps to shape the balance between immune regulatory (Treg) and pro-inflammatory (Th17) T cells.

Lee YK, et al. Science 2010

Development and Maintenance of Intestinal Microbiota

Gut microbiota, probiotics, prebiotics and synbiotics – keys to health and longevity (by Vincent Giuliano)

• A key determinant in the development of the human microbiota is likely to be exposure at birth. There are substantial differences between infants according to delivery route. • This component is mainly composed within 1 week after birth, but this may be changed regarding to the host factors and environmental factors within 1 year old. Then the component will be same as adult after 1 year old.

Gastrointestinal microbiota and Asthma (KOALA cohort study)

Van Nimwegen FA, et al. JACI 2011;128:948-55

Reduced Diversity of the Intestinal Microbiota during Infancy Is Associated with Increased Risk of Allergic

Disease at School Age (COPSAC cohort study)

Bisgaard H, et al. J Allergy Clin Immunol 2011

Immune Regulation by Intestinal Microbiota

Rachel M Mcloughlin, et al. JACI 2011

Factors to Influence Intestinal Microbiota in Infant

Microbiota

in infancy

Delivery

mode

Gestational

age

BMF and

diet

Antibiotics

and drug

Probiotics

Imbalance of Intestinal Microbiota

Rachel M Mcloughlin et al. JACI 2011

Factors to Influence Intestinal Microbiota in Infant : Delivery Mode

Key determinant in the development of the human microbiota is

likely to be exposure at birth.

Analysis of the microbiota of the newborn infants within 20

minutes of the delivery reveals that although its initial

composition is essentially uniform across different body sites,

there are substantial differences between infants according to

delivery mode.

Whereas the microbiota of vaginally delivered infants is similar to

that of the mother’s vagina or GI tract, the microbiota of infants

delivered by means of cesarean section is similar to that of the

mother’s skin or medical facility.

Mode(Vag Deli) and place(Home Deli) of delivery affect the gastrointestinal microbiota composition(C difficile), which subsequently influences the risk of atopic manifestations. (KOALA, JACI 2011)

6m AD incidence

OR p-value aOR P-value

FHx and C-sec delivery : combined analysis

FHx (+), C-sec (+) 2.685(1.364-5.285) 0.004 3.311(1.485-7.384) 0.003

FHx (+), C-sec (-)

2.140(1.194-3.834) 0.011 2.436(1.250-4.748) 0.009

FHx (-), C-sec (+) 1.212(0.588-2.495) 0.603 1.423(0.615-3.290) 0.410

FHx (-), C-sec (-) 1.00 0.010 1.00

12m AD incidence


FHx and C-sec delivery : combined analysis

FHx (+), C-sec (+)

3.071(1.428-6.605) 0.004 4.277(1.707-10.714) 0.002

FHx (+), C-sec (-) 1.410(0.689-2.884) 0.347 1.422(0.633-3.193) 0.394

FHx (-), C-sec (+)

0.754(0.294-1.935) 0.558 0.998(0.355-2.810) 0.998

FHx (-), C-sec (-) 1.00 0.011 1.00 0.010

Mode of Delivery and Atopic Dermatitis : COCOA study

Delivery Mode and Atopic Dermatitis(127/687=18.5%) at 6 mo of age


COCOA data 2012 (In prep) 이소연 등, 대알 2012 춘계학술대회 구연 발표

Mode of Delivery and CD14 Gene (C-159T) in Atopic Dermatitis : COCOA study


COCOA data 2012 (In prep)

12m AD incidence OR p-value aOR P-value

CD14 vs Delivery mode

Vag Deli/ CD14 (TT)

1.00 0.091 1.00 0.094

Vag Deli/ CD14 (TC+CC)

1.175(0.551-2.506) 0.677 1.286(0.517-3.196) 0.588

C-sec/ CD14 (TT)

2.423(0.835-7.028) 0.103 2.814(0.804-9.847) 0.105

C-sec/ CD14 (TC+CC)

2.497(1.028-6.064) 0.043 3.415(1.116-10.447)

0.031

이소연 등, 대알 2012 춘계학술대회 구연 발표

Mode of Delivery and Cord Blood Cytokines: a Birth Cohort Study

Ly NP, et al. Clin Mol Allergy 2006

Correlation between Maternal Stool Bacteria and Cord Blood Cytokine Secretion by Mode of Delivery

Ly NP, et al. Clin Mol Allergy 2006

Vaginal Delivery Cesarean Delivery

Antibiotics use in early life may be associated with the

development of allergic diseases.

Any factors that alter the maternal microbiota on the skin or

vagina around the time of birth could have a profound effect on

neonatal colonization.

Mechanism:

Antibiotics use suppress the infection, which may be

associated with the development of allergic diseases.

Antibiotics use in early life may change the intestinal

microbiota, which affect the development of T reg cell and

deviate the development of immune system.

But the microbiota are usually recovered soon after the cessation

of the antibiotics use in the intestine.

The possibility of reverse causation in asthmatic children

Factors to Influence Intestinal Microbiota in Infant : Antibiotics

0

0.5

1

1.5

2

2.5

Asthma diagnosis AR diagnosis

aOR

Antibiotics use in infancy and allergic ds

(Hygiene hypothesis study)

Antibiotics Use(-)

Antibiotics Use(+)

P< 0.05 P< 0.05

Factors to Influence Intestinal Microbiota in Infant : Antibiotics

Seo Ju-Hee, et al. JACI 2012 (In submission)

Parental History of Allergic Disease vs Use of Antibiotics in Infancy

aOR

Antibiotics in infancy

(aOR = 1.91-1.94) - + - + Parental allergic ds (aOR = 2.64-3.52) - - + +

0

1

2

3

4

5

6

7

AR Sx within 12mo

Current AR

Ref.

2.63 (2.11-3.29)

3.53

(2.74-4.55)

6.10

(4.17-7.89)

2.15

(1.69-2.74)

2.24

(1.69-2.96)

4.39

(3.46-5.56)

Kim Woo-Kyung, Kwon Ji-Won, et al. JACI 2012 (In Press)

Antibiotics Use in Pregnancy and Atopic Dermatitis : COCOA study

Antibiotics Use in Pregnancy and Atopic Dermatitis(127/687=18.5%) at 6 mo of age


COCOA data 2012 (In Prep)

6m AD incidence


FHx and Antibiotcs use at pregnancy : combined analysis

FHx (-), Antibiotics(-) 1.00 0.013 1.00 0.008

FHx (-), Antibiotics(+) 1.483(0.572-3.847) 0.418 2.065(0.691-6.173) 0.194

FHx (+), Antibiotics(-) 1.960(1.253-3.066) 0.003 2.375(1.369-4.120) 0.002

FHx (+), Antibiotics(+) 2.472(1.168-5.233) 0.018 3.436(1.286-9.175) 0.014

12m AD incidence


FHx and Antibiotcs use at pregnancy : combined analysis

FHx (-), Antibiotics(-) 1.00 0.056 1.00 0.161

FHx (-), Antibiotics(+) 2.635(0.862-8.048) 0.089 2.525(0.788-8.091) 0.119

FHx (+), Antibiotics(-) 1.688(1.031-2.762) 0.037 1.596(0.914-2.787) 0.100

FHx (+), Antibiotics(+) 2.432(0.968-6.111) 0.059 2.339(0.756-7.237) 0.140


COCOA data 2012 (In prep)

12m AD 발생율


IL13 & 임신중 항생제복용

복용안함/IL13 (GG)

1.00 0.330 1.00 0.191

복용안함/IL13 (GA+AA)

1.157 (0.638-2.100)

0.631 1.606 (0.811-3.180)

0.174

복용했음/IL13 (GG)

2.115 (0.618-7.243)

0.233 1.300 (0.278-6.076)

0.738

복용했음/IL13 (GA+AA)

2.538 (0.782-8.241)

0.121 3.877 (1.061-14.164)

0.040


Antibiotics Use in Pregnancy and IL-13 Gene (G+2044A) in Atopic Dermatitis : COCOA study


Role of Antibiotics and Fungal Microbiota in Driving Pulmonary Allergic Responses

Noverr MC, et al. Inf & Immunity 2004 Untreated Mice Treated Mice

Early Life Risk Factors Regarding to Intestinal Microbiota for Allergic Disease or Atopy

aOR

Risk factor number

▶Risk factors : 1. Caesarean delivery 2. Formula feeding 3. Antibiotics use during infancy

• Asthma(aOR, 95%CI: 2.234, 0.858-5.871), AR(2.167, 1.037-4.376), AD(1.392, 0.665-2.911), Atopy(2.469, 1.147-5.317) • Adjusted for age, sex, parental income, parental history of allergic diseases, BMI, and exposure to tobacco smoke. Seo Ju-Hee, et al. JACI Sumitted 2012

Gene-environmental interaction between the risk factors (c/sec, formula feeding, antibiotic use )

and CD14(-159C/T) polymorphism

aOR

*

*

Allergic rhinitis diagnosis

Adjusted for age, sex, parental income, parental history of allergic diseases, BMI, and exposure to tobacco smoke. Seo Ju-Hee, et al. JACI submitted 2012

6.571

1.749

Gene-environmental interaction between the risk factors (c/sec, formula feeding, antibiotic use )

and IL-13(+2044G/A) polymorphism

aOR

*

*

Allergic rhinitis diagnosis

Adjusted for age, sex, parental income, parental history of allergic diseases, BMI, and exposure to tobacco smoke. Seo Ju-Hee, et al. JACI submitted 2012

1.889

4.807

Changes in the Composition of the Human Fecal Microbiome After Bacteriotherapy for Recurrent

Clostridium Difficile-Associated Diarrhea: 61 old / F

• Clostridium difficile-associated disease (CDAD) is the major known cause of antibiotic-induced diarrhea and colitis, and the disease is thought to result from persistent disruption of commensal gut microbiota. Bacteriotherapy by way of fecal transplantation can be used to treat recurrent CDAD, which is thought to reestablish the normal colonic microflora. However, limitations of conventional microbiologic techniques have, until recently, precluded testing of this idea.

• In this study, we used terminal-restriction fragment length polymorphism and 16S rRNA gene sequencing approaches

• By 14 days posttransplantation, the fecal bacterial composition of the recipient was highly similar to that of the donor and was dominated by Bacteroides spp. strains and an uncharacterized butyrate producing bacterium. The change in bacterial composition was accompanied by resolution of the patient’s symptoms. The striking similarity of the recipient’s and donor’s intestinal microbiota following after bacteriotherapy suggests that the donor’s bacteria quickly occupied their requisite niches resulting in restoration of both the structure and function of the microbial communities present.

Khoruts A, et al. J Clin Gastroenterol 2010

Gene-Environment Interaction

Environment Individual variable

Susceptibility

Gene-Environmental interaction

Allergic diseases (asthma, AD, AR)

New wheeze, new BHR and distance to main road

New wheeze :

wheeze ever : negative -> positive (97/750, 12.9%)

(1st yr) (3rd yr)

P for trend = 0.085* aOR = 1.227 (0.972-1.548)

Adjusted for sex, age, BMI, parental allergy history, maternal education, ETS, BMF

Kim Byoung-Ju, et al. KAAACI 2011 meeting Abstract #118

New BHR :

PC20 < 8 mg/dl : negative -> positive (45/792, 5.7%)

(1st yr) (3rd yr)

0

1

2

3

4

5

6

7

>200 m 100-200 m 50-100 m < 50 m

4.0%

5.8%

6.9% 7.0%

13.5%

9.3%

13.6%

22.0%

0

5

10

15

20

25

>200 m 100-200 m 50-100 m < 50 m

P for trend = 0.030* aOR = 1.475 (1.039-2.093)

New development of wheeze and BHR are closely related with living of the distance to main road, which suggest that traffic related air pollution may affect the development of asthma.

Gene-environment-environment interactions among TLR4, IL-13 polymorphism and bronchiolitis

and PM10 exposure could influence the development of asthma in preschool children

Young-Ho Jung1,2, Hyung Young Kim1,2, Ju-Hee Seo1,2, Ji-Won Kwon3, Byoung-Ju Kim4, Hyo Bin Kim5, So Yeon Lee6, Gwang Cheon Jang7, Dae Jin Song8, Woo-Kyung Kim9, Jung Yeon Shim10, Jong-Han Leem11,

Hwan-Cheol Kim11, Soo-Jong Hong1,2*

1Childhood Asthma Atopy Center,Department of Pediatrics, Asan Medical Center, University of Ulsan

College of Medicine , 2Research Center for Standardization of Allergic Diseases ,3Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine,

4Department of Pediatrics, Hae-undae Paik Hospital, Inje University College of Medicine, 5Department of Pediatrics, Sanggye Paik Hospital, Inje University College of Medicine, 6Department of Pediatrics, Hallym University Secred Heart Hospital, 7Department of Pediatrics, National Health Insurance Corporation Ilsan

Hospital, 8Department of Pediatrics, Korea University Guro Hospital, 9Department of Pediatrics, Seoul Paik Hospital, Inje University College of Medicine, 10Department of Pediatrics, Kangbuk Samsung

Hospital, Sungkyunkwan University College of Medicine, 11Department of Occupational and Environmental Medicine, Inha University Hospital

알레르기질환표준화연구센터

Research Center for Standardization of Allergic Diseases

대알 2012 춘계학술대회 구연 발표



Gene TLR4 IL-13

PM10

Asthma

Bronchiolitis

Figure 1. Combination analysis between PM10 exposure and past history of bronchiolitis regarding to asthma diagnosis

Data were calculated by logistic regression multivariate analysis aOR = adjusted odds ratio; CI = confidence interval; PM10 = particulate matter 10 aOR = adjusted by age, gender, body mass index, degree of maternal education * P < 0.001



0

1

2

3

4

5

6

7

8

1 2 3 4

Bronchiolitis (-) High PM10 (-) aOR Ref. 95% CI

(-) (+) 0.91 0.42-1.97

(+) (-) 2.58 0.91-7.30

(+) (+) 6.99 2.99-16.32

*

정영호 등, 대알 2012 춘계학술대회 구연 발표

Figure 2. Combination analysis between PM10 exposure and past history of bronchiolitis regarding to asthma diagnosis depending on TLR4 (rs1927911) polymorphism

Data were calculated by logistic regression multivariate analysis aOR = adjusted odds ratio; CI = confidence interval; PM10 = particulate matter 10 aOR = adjusted by age, gender, body mass index, degree of maternal education * P < 0.05




(-) (+) 0.80/1.07 0.17-3.83 /0.35-3.25

(+) (-) 0.74/5.39 0.06-8.83 /0.90-32.46

(+) (+) 9.27/6.62 1.43-60.06 /1.64-26.81

*

0

2

4

6

8

10

1 2 3 4

CC

CT+TT

*

*


Figure 3. Combination analysis between PM10 exposure and past history of bronchiolitis regarding to asthma diagnosis depending on IL-13 (rs20541) polymorphism

Data were calculated by logistic regression multivariate analysis aOR = adjusted odds ratio; CI = confidence interval; PM10 = particulate matter 10 aOR = adjusted by age, gender, body mass index, degree of maternal education * P < 0.05 알레르기질환표준화연구센터



(-) (+) 0.50/2.25 0.14-1.82 /0.64-7.85

(+) (-) 2.55/3.58 0.43-15.00 /0.54-23.92

(+) (+) 2.61/17.77 0.51-13.40 /3.81-82.91

*

0

2

4

6

8

10

12

14

16

18

20

1 2 3 4

GG

AG+AA

*


Conclusion

The prevalence of atopic dermatitis is increasing in Korea.

Environmental exposures in infancy or at pregnancy and genetic

background may be an important independent risk factors in the

development of allergic diseases.

Especially intestinal microbiome status in early life may be critical

to the development of allergic diseases.

Primary prevention of asthma and allergic diseases will only be

possible when the genetic and environmental factors including

intestinal microbiome or the nature of GE interaction have been

identified.

Thus, natural course, risk factors, diagnostic factors, prevention of

childhood asthma and allergic diseases should be identified by

the new hypothesis driven cohort study.

Acknowledgement Ulsan University, Asan Medical Center Young Ho Jung MD Hyung Yung Kim MD Jinho Yu MD, PhD Ja-Hyung Kim MD, PhD Bong-Seong Kim MD, PhD Asan Institute for Life Sciences Mi-Jin Kang BS Ho-Sung Yu Hee-Sook Kim Seung-Hwa Lee Young-Jun Kim Ha-Jeong Kim PhD Ji-Yeon Ko RN Hae-Rim Shin BS Jae-Hee Kim BS Yoo-Jin Lee Kyung-Shin Lee BS Seo-Ah Hong PhD

Ju-Hee Seo MD

Ji-Won Kwon MD

Byoung-Ju Kim MD, PhD

Hyo-Bin Kim MD, PhD

Woo-Kyung Kim MD, PhD

So-Yeon Lee MD, PhD

Dae-Jin Song MD, PhD

Kwang-Cheon Jang MD, PhD

Jeong-Yeon Shim MD, PhD

Soo-Young Lee MD, PhD

Kyung-Won Kim MD, PhD

Yoon-Ho Shin MD, PhD

Kang-Mo Ahn MD, PhD

Kang-Seo Park MD, PhD

• COCOA cohort team

• KOREA cohort team

• AAA cohort team

• CHEER cohort team

• Hygiene hypothesis study team

• Atopy free Seoul study team

• Childhood Asthma and Atopy Center

• Nutrition(Se Young Oh), Environment(Cheol Min Lee, Jong-Han Leem), Psychiatry(Yee Jin Shin), Statistics(Ho Kim)

Thank you for your attention !

Documents

Gene and Environment in the Development of Allergic Diseases · 2014-10-22 · Comparison of wheeze and atopy between Seoul and Jeongup area Lee So-Yeon, et al. Int Arch Allergy Immunol