  Recent evidence highlights the usefulness of DNA methylation (DNAm)
biomarkers as surrogates for exposure to risk factors for non-communicable
diseases in epidemiological studies and randomized trials. DNAm variability has
been demonstrated to be tightly related to lifestyle behavior and exposure to
environmental risk factors, ultimately providing an unbiased proxy of an
individual state of health. At present, the creation of DNAm surrogates relies
on univariate penalized regression models, with elastic-net regularizer being
the gold standard when accomplishing the task. Nonetheless, more advanced
modeling procedures are required in the presence of multivariate outcomes with
a structured dependence pattern among the study samples. In this work we
propose a general framework for mixed-effects multitask learning in presence of
high-dimensional predictors to develop a multivariate DNAm biomarker from a
multi-center study. A penalized estimation scheme based on an
expectation-maximization algorithm is devised, in which any penalty criteria
for fixed-effects models can be conveniently incorporated in the fitting
process. We apply the proposed methodology to create novel DNAm surrogate
biomarkers for multiple correlated risk factors for cardiovascular diseases and
comorbidities. We show that the proposed approach, modeling multiple outcomes
together, outperforms state-of-the-art alternatives, both in predictive power
and bio-molecular interpretation of the results.
