Fungal DNA health studies and patient cases

Transcription

1 Fungal DNA health studies and patient cases Guihong Cai Occupational hygienist (AMM) and Researcher (pos-doc): Department of Allergy and Sleep Research, Uppsala University.

2 Contents Fungal DNA method (QPCR) Malaysia School Study HESE study: Health Effects of School Environment Investigation of patient cases with DNA method EpiHealth study 2

3 Molds/Dampness and Health Over 100 epidemiological studies showed that dampness/moulds exposure are at increased risk of respiratory symptoms/infection, asthma, headache, eye symptoms or SBS, but depending on the species involved (Quansah, et al ; WHO 2009; IOM 2004). Viable/Non-viable moulds could be risk to health, both need to accurately measure specially the species composition and concentrations. 3

4 Methods for Mould Measurements Traditional culture/microscope counting cells method: Economical/on species level, but requires qualified person or only detect the viable/cultural moulds. Quantitative Polymerase Chain Reaction (qpcr): Fast, quantify viable/non-viable moulds, low detection limit and high accuracy, can detect general (e.g. Asp/Pen) and specific fungal DNA sequences (e.g. A. versicolor). Fig. qpcr and culture-based methods, 4 Aspergillus spp. (GM levels), home dust samples in Cincinnati, OH. (Meklin et al., 2004) 4

5 Particles/Dust sampling 3. Vacuum cleaner: ALK filters 1. Cotton Swab: 60 cm 2 upper doorframe 2. Petri dish: Open to indoor air 2017/2/3

6 Malaysia School Study (I): Population 8 schools (32 classrooms), 462 pupils participated. Participation rate: 96 %; Mean age: 14yr (range 14-16). Johor Bahru

7 Fungal DNA Cotton swab (CE/m 2 ) Petri dish (CE/m 2 /day) Total fungal DNA (GM±GSD 1 ) ± ±1.9 Asp/Pen DNA (GM±GSD 1 ) ± ±3.0 A. versicolor DNA (Arithmetic Mean) S. chartarum DNA (Arithmetic Mean) Streptomyces DNA (Arithmetic Mean) Result (I-1): Fungal DNA Levels GM: Geometric mean, GSD: Geometric standard deviation. 2017/2/3 7

8 Result (I-2) Fungal DNA/Health Associations Fungal DNA (Petri dish dust) Wheeze Daytime breathlessness Doctordiagnosed asthma 1 Total fungal DNA 0.71 ( ) 1.02 ( ) 1.32 ( ) 2 Asp/Pen DNA 0.84 ( ) 0.91 ( ) 0.99 ( ) 3 A. versicolor DNA 1.19 ( )** 1.14 ( )* 1.04 ( ) 4 S. chartarum DNA 1.03 ( ) 0.88 ( )* 1.01 ( ) 5 Streptomyces DNA 0.95 ( ) 1.05 ( ) 1.18 ( )* No associations between fungal DNA in swab samples and respiratory health.

9 Fungal DNA in HESE study

10 log-total fungal DNA (CE/g dust) Result (II-1): log-total Fungal DNA Levels Siena, Italy Udine, Italy Oslo, Norway Uppsala, Sweden Århus, Denmark Reims, France Simoni et al., /2/3 10

11 Result (II-2) Associations by Tertile: Total Fungal DNA/Health 4 2 nd tertile Dose Effect 3 rd tertile Wheeze Dry cough at night Rhinitis Cough Risk (Odds Ratio and 95 % Confidence Interval) for reporting respiratory disorders by tertile exposure to log-total funal DNA (CE/g). Analyses accounted for gender, age, ETS, and lifetimeasthma). Reference category for moulds exposure: 1 st tertile of the distribution 2017/2/3 11

12 Result (II-3) Associations by Tertile: Fungal DNA/Health 4 2 nd tertile 3 rd tertile Dose Effect 6 2 nd tertile 3 rd tertile Wheeze dry cough at night Rhinitis Cough Risk (Odds Ratio and 95 % Confidence Interval) for reporting respiratory disorders by tertile exposure to log-asp/pen DNA (CE/g) Wheeze Dry cough at night Rhinitis Cough Risk (Odds Ratio and 95 % Confidence Interval) for reporting respiratory disorders by tertile exposure to log-a. versicolor DNA (CE/g). Analyses accounted for gender, age, ETS, and lifetimeasthma). Reference category for moulds exposure: 1 st tertile of the distribution. 2017/2/3 12

13 Result (II-4) Lung Function Linear regression models Crude adjusted a Log-total VM FEV (-0.24;0.02) bl (-0.07;0.18) FVC (-0.36;-0.06) 0.01 (-0.12 ;0.14) Log-A. versicolor DNA FEV (-0.16;-0.01)* (-0.14 ;0.06) FVC (-0.25;-0.06)** (-0.21;0.01) bl Log-Streptomyces DNA FEV (-0.21;-0.04)** (-0.23 ;-0.05)* FVC (-0.26;-0.05)** (-0.23 ;-0.04)** FEV 1 = forced expiratory volume in 1 second; FVC = forced vital capacity. p<0.05, ** p<0.01, bl 0.05 < p < 0.1. a Adjusted for gender, age, height, passive smoking at home, and any of lifetimeasthma, dry cough at night, cough. 2017/2/3 13

14 Anozona DNA method 1. Total fungi: common indoor mould. 2. Asp/Pen: like warm, allergic 3. A.versicolor: good indicator of mild dampness in nordic country (no need high humidity), health problems: inflamation in mice experiment ( spores) (Jussila and Komulainen, 2002). 4. S.chartarum: well known toxic spp. 5. Streptomyces: health problems, common spp. and soil odour Fungal DNA analysis done by anozona AB, Uppsla 2017/2/3 14

15 Patient case 1: home environment Two-floors wooden house (1932) with water damage at the 2 nd floor (damaged floorboards were removed). By visiting: we found moisture damaged signs in bathrooms at the 1st floor Fungal DNA Living room (at Sleeping room 1st floor) (at 2nd floor) Total fungal DNA Asp/Pen DNA A. versicolor DNA 22 5 S. chartarum DNA 0 0 Streptomyces DNA /2/3 15

16 Patient case 2: day care centre Patient is experiencing the most symptoms and smell in the Haren section. Significantly less health problems and odor in Grodan section. Fungal DNA Grodan section Haren section Total fungal DNA Asp/Pen DNA A. versicolor DNA 2 0 S. chartarum DNA 0 0 Streptomyces DNA /2/3 16

17 Conclusions With DNA method: will always find indoor mold, and sometimes found the hidden damage, but the content was higher in the room where the patient is experiencing more symptoms. With DNA method: can also get information about the renovation of dampness has been successful. The method is useful especially when there is no building technical investigation. When communicating with the patient: There is not a simple relationship between the level of fungal DNA and health, and emphasizes that this method is a way to better assess whether the building is a damp building. 2017/2/3 17

18 0 Prevalence of sleep problems 20 % 40 % 60 % 80 % Prevalence of central obesity My research at Dep. Allergy and Sleep Research EpiHealth study: Primary objective was to study how interactions between lifestyle factors and genotypes contribute to the development of common disorders (eg. respiratory diseases, lung function) in humans (started in 2011) with participants, age (45-75 ys old). My research now: Study the body mass index (BMI) scores, weight gain, obesity and central obesity associated with the prevalence of sleep disturbances and the impact of sleep duration and insomnia symptoms when other lifestyle variables were taking into account Habitual sleeping time (hours/night) DIS DMS EMA 2017/2/ % 40 % 50 % 60 % 70 % Habitual sleeping time (hours/night)

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