what does asthma look like for different people? ……asthma phenotypes
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What does asthma look like for different people? ……Asthma phenotypes. Thurs September 19 11.30am-12.00pm. What is a phenotype ?. The observable characteristics of an individual resulting from the interaction of its genotype with the environment. What is a phenotype ?. - PowerPoint PPT PresentationTRANSCRIPT
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What does asthma look like for different people? ……Asthma phenotypes
Thurs September 19
11.30am-12.00pm
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What is a phenotype ?
The observable characteristics of an individual resulting from the interaction of its genotype with the environment
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What is a phenotype ?
Phenotype: observable qualities of an individual = gene X environment
Clinical Phenotype: Clinically important observable qualities of an organism Han MLK etal, AJRCCM 2010;182:598
Endotype: endogenous mechanism that underlies a phenotype Anderson GP, Lancet 2008; 372:1107
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Phenotypes, why bother ?
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Australia, 1979–2006
Asthma deaths Then…….. and now….
Australian Centre for Asthma Monitoring
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Asthma Control Asthma Control
Oral glucocorticosteroid
(lowest dose)
anti-IgE antibodies
as needed rapid-acting β2-agonist
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People vary in how they respond to treatment
Are you the
average ?
Or are you an
Individual ?
Zeiger RS et al. J Allergy Clin Immunol. 2006;117:45–52.
responder
Little response
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Clinical phenotypes that don’t fit Obesity…. 50% Smoking….30% Asthma-COPD Overlap Asthma in the elderly Severe Asthma…10%
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Older people with airway disease:age >55, multiple problems, irrespective of
the diagnosis
COPD Asthma Overlap OAD0
50
100 p=0.003 p=0.03
p=0.74
SG
RQ
un
its
Health Status (SGRQ) by Diagnosis
0 5 10 15 200
25
50
75
100
r=0.59p<0.0001
number of clinical management problems
HR
QO
L (
SG
RQ
)
Health Related quality of life impairment was associated with the number of management problems identified by the MDA
McDonald VM et al, Age Ageing 2011:40;42-9
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Older person with asthma
Older person with asthma
Gibson PG, McDonald VM, Marks GM. Lancet 2010; 376:803
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Airway disease in older people
Gibson PG, McDonald VM, Marks GM. Lancet 2010; 376:803
1. Multidimensional Assessment
2. Biomarkers drive Pharmacotherapy
3. Case manager
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Ester, 87 years, ♀, Asthma
Presents to clinic following admission Exacerbation of asthma and worsening
depressive symptoms Background –
Asthma since age 7 Depression, AF, HT, heart failure, TIAs,
Cataracts, GORD: CCI= 7 Never Smoker Exacerbation history – 4 courses of
antibiotics in the past 12 months
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Ester’s perspective ‘I get puffed so easily, I can’t walk up hills. I stop
doing things because my breathing gets worse, my biggest problem is getting puffed’
‘I feel useless’
‘No I don’t think rehab is for me, I don’t want that. It’s too much effort, I would rather just do exercise at home. I don’t want to do the group stuff’
Ester, 87years, ♀, Asthma
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Ester, 87years, ♀, Asthma
Body Composition
BMD -T scores = total body 0, hip -0.8 = normal
ASMMI 5.9km/m2 = normal
Slow gait speed & unable to do 5 chair rises
Pulmonary Rehab + home based resistance training 3 X week
Airway Inflammation= normal
No sputum, FENO 17.5ppb
Maintenance ICS/LABA
Systemic Inflammation= YES
CRP mg/mL 18.1
Simvastatin 20mg
Breathing dysfunction = YES
Nijmegen 37
Breath retraining
Anxiety/Depression = YES
HADS (A) 8 (D) 10
Depression management – Paroxetine 20mg + counselling
Frequent Infections Self Management Education with WAP
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OutcomesBaseline 3 months 6 months
FEV1 1.27 (77) 1.3 (81) 1.12 (66)
SGRQ 56.3 24.3 27.4
Exacerbations 4 past 12/12 0 past 3/12 0 past 6/12
6MWD 257.2 333.8 359.1
FENO 17.5 16.2 18
CRP 18.1 4.2
Nijmegen 37 41 29
ASMMI 5.9kg/m2 6kg/m2 6kg/m2
BMI 22 24.2 24.3
HADS A|D 8|10 6|4 4|6
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Treatment Effects multidimensional assessment and intervention
McDonald VM, Gibson PG et al. ERS 2010McDonald VM, Higgins I, Wood L, Gibson PG. Thorax 2013; 68:691
Rehab
Self M
x
TORCH (SFC)
UPLIFT (T
IO)
IPBM
USUAL C
AREIP
BM
-14
-12
-10
-8
-6
-4
-2
0
Ch
ang
e in
SG
RQ
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‘hidden’ phenotypesgenes and environment
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Phenotype = gene X environment
Gene = DNA
How do you tell when it is relevant?
The gene has to be doing something,And you tell that from….RNAProtein
We call that a biomarker.
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Inflammatory phenotyping
It means: Biomarker + specific treatment
+
2.69
0.90
0
0.5
1
1.5
2
2.5
3
Before omalizumab Week 52 after omalizumab
An
nu
al s
eve
re e
xace
rba
tion
rate
= reduced exacerbations
Bruselle A, Resp Med 2009
omalizumabAllergen specific IgE
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Inflammatory phenotyping It means : Biomarker + specific treatment
Anti IL5mAb =
Haldar NEJM 2009; Nair NEJM 2009
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0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Nonsevere Allergicsevere
Eosinophilicsevere
Early onsetallergic
Exac/yr
Exacerbation rate by phenotype
McDonald V, Clin Exp Allergy 2013
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Severe Asthma
Raised IgEOmalizumabItraconazole*
Anti IL-13 Mab
EosinophilicOral/IMI
CorticosteroidAnti IL5 Mab
NoneosinophilicMacrolides
* ABPA, SAFS
Inflammatory phenotyping For Refractory SevereAsthma
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Managing Asthma in Pregnancy
Biomarker:The Protein: an enzyme, iNOS.When active it produces a gas, called Nitric oxide, or FENOThat is measured in your breath
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Powell et al, Lancet 2011
Treatment guided By symptoms
Treatment guided by FeNO + symptoms
106 women With asthma
104 women With asthma
Managing Asthma in Pregnancy
OR
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Powell et al, Lancet 2011
Asthma attacks were reducedBy Half
Managing Asthma in PregnancyFENO guided treatment reduces attacks
FeNO
Symptoms
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Powell et al, Lancet 2011
210 mothers with asthma,
214 beautiful babies
What happened to them?
0
2
4
6
8
10
12
14
16
18
NICU
Less babies in NICUFewer attacks bronchiolitis
Managing Asthma in PregnancyWhat about the babies ?
FeNO
%
Mattes J , Thorax 2013, in press
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Fewer courses Of steroidsWere neededFor wheezing attacks
Oral Corticosteroids
Mattes J , Thorax 2013, in press
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‘hidden’ phenotypes,genes and environment
Biomarker is FENO
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‘hidden’ phenotypes,genes and environment
‘gene chips’
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Transcriptomics: Baines K, JACI, 2011
187 genes
24 genes 258 genes
ASTHMA
Non-GranulocyticGranulocytic
Eosinophilenriched
Neutrophilenriched
1 2
3
EOS NEUT PAUCI
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Sputum gene expression Sputum gene expression biomarkers for asthma phenotypebiomarkers for asthma phenotype
Microarray screening approach to identify sputum biomarkers for eosinophilic, neutrophilic and paucigranulocytic asthma.
Candidate biomarkers (n=35) were tested and 27 validated using qPCR in 3 studies (discovery, clinical validation, ICS response). A combination of 6 genes including CLC, CPA3, DNASE1L3, IL1B, ALPL, CXCR2, can predict asthma inflammatory phenotypes from each other and healthy controls.
Eos NeutHC
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Gene signature can predict ICS Gene signature can predict ICS responseresponse
• Steroid response trial: n=71 people with asthma treated with 1000ug fluticasone per day, 28 days
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‘hidden’ phenotypes,genes and environment
‘gene chips’
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Phenotypes….Now to next ?Now: Mortality has reduced Control has improved Overdiagnosis Overtreatment People are still unwell
Next: ? Cure Prevention New treatment:
breakthroughs Lifestyle