title: A probabilistic model of biological ageing of the lungs for analysing the effects of smoking, asthma and COPD.
abstract: BACKGROUND
  Although a large body of literature is available that describes the effects of smoking, asthma and COPD on lung function, most studies are restricted to a small age range and to one factor. As a consequence, available results are incomplete and often difficult to compare, also due to the ways the effects are expressed. Furthermore, current approaches consider one type of measurement only or several types separately.
  
  
  METHODS
  We propose a probabilistic model that expresses the effects as number of years added to chronological age or, in other words, that estimates the biological age of the lungs. Using biological age as a measure of the effects has the advantage of facilitating the understanding of their severity and comparison of results. In our model, chronological age and other factors affecting the health status of the lungs generate biological age, which in turn generates lung function measurements. This structure enables the use of multiple types of measurement to obtain a more precise estimate of the effects and parameter sharing for characterization over large age ranges and of co-occurrence of factors with little data. We treat the parameters that model smoking habits and lung diseases as random variables to obtain uncertainty in the estimated effects.
  
  
  RESULTS
  We use the model to investigate the effects of smoking, asthma and COPD on the TwinsUK Registry. Our results suggest that the combination of smoking with lung disease(s) has higher effect than smoking or lung disease(s) alone, and that in smokers, co-occurrence of asthma and COPD is more detrimental than asthma or COPD alone.
  
  
  CONCLUSIONS
  The proposed model or other models based on a similar approach could be of help in improving the understanding of factors affecting lung function by enabling characterizations over large age ranges and of co-occurrence of factors with little data and the use of multiple types of measurement. The software implementing the model can be downloaded at the first author's webpage.
authors.affiliation: Microsoft Research Cambridge, , 21 Station Road, Cambridge CB1 2FB, UK. silvia@microsoft.com
authors.forename: Silvia
authors.initials: S
authors.lastname: Chiappa
authors.forename: John
authors.initials: J
authors.lastname: Winn
authors.forename: Ana
authors.initials: A
authors.lastname: Viñuela
authors.forename: Hannah
authors.initials: H
authors.lastname: Tipney
authors.forename: Timothy David
authors.initials: TD
authors.lastname: Spector
mesh_terms: D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D001249:Asthma / Q000453:epidemiology*; D015897:Comorbidity; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D015994:Incidence; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D016016:Proportional Hazards Models; D029424:Pulmonary Disease, Chronic Obstructive / Q000453:epidemiology*; D012042:Registries / Q000706:statistics & numerical data*; D018570:Risk Assessment; D012307:Risk Factors; D012907:Smoking / Q000453:epidemiology*; D006113:United Kingdom / Q000453:epidemiology; D055815:Young Adult