Epigenome-wide DNA methylation signature of plasma zinc and their mediation roles in the association of zinc with lung cancer risk

Essential trace element zinc is associated with decreased lung cancer risk, but underlying mechanisms remain unclear. This study aimed to investigate role of DNA methylation in zinc-lung cancer association. We conducted a case-cohort study within prospective Dongfeng-Tongji cohort, including 359 incident lung cancer cases and a randomly selected sub-cohort of 1399 participants. Epigenome-wide association study (EWAS) was used to examine association of plasma zinc with DNA methylation in peripheral blood. For the zinc-related CpGs, their mediation effects on zinc-lung cancer association were assessed; their diagnostic performance for lung cancer was testified in the case-cohort study and further validated in another 126 pairs of lung cancer case-control study. We identified 28 CpGs associated with plasma zinc at P < 1.0 × 10^-5 and seven of them (cg07077080, cg01077808, cg17749033, cg15554270, cg26125625, cg10669424, and cg15409013 annotated to GSR, CALR3, SLC16A3, PHLPP2, SLC12A8, VGLL4, and ADAMTS16, respectively) were associated with incident risk of lung cancer. Moreover, the above seven CpGs were differently methylated between 126 pairs of lung cancer and adjacent normal lung tissues and had the same directions with EWAS of zinc. They could mediate a separate 7.05%∼22.65% and a joint 29.42% of zinc-lung cancer association. Compared to using traditional factors, addition of methylation risk score exerted improved discriminations for lung cancer both in case-cohort study [area under the curve (AUC) = 0.818 vs. 0.738] and in case-control study (AUC = 0.816 vs. 0.646). Our results provide new insights for the biological role of DNA methylation in the inverse association of zinc with incident lung cancer.

Cumulative risk regression in case-cohort studies using pseudo-observations

Case-cohort studies are useful when information on certain risk factors is difficult or costly to ascertain. Particularly, a case-cohort study may be well suited in situations where several case series are of interest, e.g. in studies with competing risks, because the same sub-cohort may serve as a comparison group for all case series. Previous analyses of this kind of sampled cohort data most often involved estimation of rate ratios based on a Cox regression model. However, with competing risks this method will not provide parameters that directly describe the association between covariates and cumulative risks. In this paper, we study regression analysis of cause-specific cumulative risks in case-cohort studies using pseudo-observations. We focus mainly on the situation with competing risks. However, as a by-product, we also develop a method by which absolute mortality risks may be analyzed directly from case-cohort survival data. We adjust for the case-cohort sampling by inverse sampling probabilities applied to a generalized estimation equation. The large-sample properties of the proposed estimator are developed and small-sample properties are evaluated in a simulation study. We apply the methodology to study the effect of a specific diet component and a specific gene on the absolute risk of atrial fibrillation.

A method making fewer assumptions gave the most reliable estimates of exposure-outcome associations in stratified case-cohort studies

Objective

A case–cohort study is an efficient epidemiological study design for estimating exposure–outcome associations. When sampling of the subcohort is stratified, several methods of analysis are possible, but it is unclear how they compare. Our objective was to compare five analysis methods using Cox regression for this type of data, ranging from a crude model that ignores the stratification to a flexible one that allows nonproportional hazards and varying covariate effects across the strata.

Study Design and Setting

We applied the five methods to estimate the association between physical activity and incident type 2 diabetes using data from a stratified case–cohort study and also used artificial data sets to exemplify circumstances in which they can give different results.

Results

In the diabetes study, all methods except the method that ignores the stratification gave similar results for the hazard ratio associated with physical activity. In the artificial data sets, the more flexible methods were shown to be necessary when certain assumptions of the simpler models failed. The most flexible method gave reliable results for all the artificial data sets.

Conclusion

The most flexible method is computationally straightforward, and appropriate whether or not key assumptions made by the simpler models are valid.