Investigating the association of environmental exposures and all-cause mortality in the UK Biobank using sparse principal component analysis

tMulticollinearity refers to the presence of collinearity between multiple variables and renders the results of statistical inference erroneous (Type II error). This is particularly important in environmental health research where multicollinearity can hinder inference. To address this, correlated variables are often excluded from the analysis, limiting the discovery of new associations. An alternative approach to address this problem is the use of principal component analysis. This method, combines and projects a group of correlated variables onto a new orthogonal space. While this resolves the multicollinearity problem, it poses another challenge in relation to interpretability of results. Standard hypothesis testing methods can be used to evaluate the association of projected predictors, called principal components, with the outcomes of interest, however, there is no established way to trace the significance of principal components back to individual variables. To address this problem, we investigated the use of sparse principal component analysis which enforces a parsimonious projection. We hypothesise that this parsimony could facilitate the interpretability of findings. To this end, we investigated the association of 20 environmental predictors with all-cause mortality adjusting for demographic, socioeconomic, physiological, and behavioural factors. The study was conducted in a cohort of 379,690 individuals in the UK. During an average follow-up of 8.05 years (3,055,166 total person-years), 14,996 deaths were observed. We used Cox regression models to estimate the hazard ratio (HR) and 95% confidence intervals (CI). The Cox models were fitted to the standardised environmental predictors (a) without any transformation (b) transformed with PCA, and (c) transformed with SPCA. The comparison of findings underlined the potential of SPCA for conducting inference in scenarios where multicollinearity can increase the risk of Type II error. Our analysis unravelled a significant association between average noise pollution and increased risk of all-cause mortality. Specifically, those in the upper deciles of noise exposure have between 5 and 10% increased risk of all-cause mortality compared to the lowest decile.</p

Annual prevalence of the 10 most common comorbidities in patients with incident cardiovascular disease between 2000 and 2014.

<p>CKD, chronic kidney disease.</p

Age-standardised prevalence of comorbidities in women and men.

<p>The top 20 comorbidities ranked by sex-specific age-standardised prevalence between 2000 and 2014 are shown for men (blue) and women (red). CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; PAD, peripheral arterial disease.</p

Annual crude and age/sex-standardised prevalence of number of comorbidities in incident cardiovascular disease patients.

<p>(A) Crude prevalence. (B) Age/sex-standardised prevalence. Number labels for each line refer to the number of comorbidities.</p

Age/sex-standardised prevalence of comorbidities in patients with incident cardiovascular disease (<i>n =</i> 229,205).

<p>Age/sex-standardised prevalence of comorbidities in patients with incident cardiovascular disease (<i>n =</i> 229,205).</p

Baseline characteristics of patients with CVD at time of diagnosis.

<p>Baseline characteristics of patients with CVD at time of diagnosis.</p

Comorbidities selected.

<p>Comorbidities selected.</p

Annual age/sex-standardised incidence of CVD, IHD, and stroke/TIA.

<p>Overall CVD (red line); IHD (including acute myocardial infarction and angina; green line); stroke/TIA (blue line). CVD, cardiovascular disease; IHD, ischaemic heart disease; TIA, transient ischaemic attack.</p

Age/sex-standardised prevalence of number of comorbidities in patients with incident cardiovascular disease by socioeconomic status.

<p>Socioeconomic status is split by quintiles of the Index of Multiple Deprivation for 2015, where 1 = the least deprived fifth, and 5 = the most deprived fifth.</p

Age-specific ranking of the top 10 most prevalent comorbidities.

<p>Age-specific ranking of the top 10 most prevalent comorbidities.</p