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Despite increased sophistication in DNA methylation (DNAm) measurement and methods, conducting studies in specific populations such as the hematopoietic stem cell transplant (HCT) population, presents unique challenges and study design considerations. In this article, we explain the motivation for investigating DNAm in the HCT population, highlighting important study design features and key findings in a longitudinal prospective pilot study of DNAm in 32 patients undergoing autologous HCT in Central Virginia, USA. We also discuss limitations and challenges to generating robust results. We observed that HCT does not prevent high-quality DNA from being extracted from whole blood for DNAm research and that longitudinal prospective studies that span pre- and 2-months post-HCT are feasible. Critically, we did not observe significant impacts of cancer diagnosis, time since transplant, age, or chromosomal sex on overall DNAm data dimensionality. These observations demonstrate that while extreme care is required to ensure generalizable, accurate, and interpretable results, researchers should not avoid HCT-DNAm research simply for fear that the transplant procedure or presence of a cancer diagnosis will prevent meaningful conclusions from being drawn. DNAm is an attractive biomarker that is understudied in patients undergoing HCT and needs to expand to improve precise prediction of HCT outcomes. [ABSTRACT FROM AUTHOR]

Maternal age is an established predictor of preterm birth independent of other recognized risk factors. The use of chronological age makes the assumption that individuals age at a similar rate. Therefore, it does not capture interindividual differences that may exist due to genetic background and environmental exposures. As a result, there is a need to identify biomarkers that more closely index the rate of cellular aging. One potential candidate is biological age (BA) estimated by the DNA methylome. This study investigated whether maternal BA, estimated in either early and/or late pregnancy, predicts gestational age at birth. BA was estimated from a genome-wide DNA methylation platform using the Horvath algorithm. Linear regression methods assessed the relationship between BA and pregnancy outcomes, including gestational age at birth and prenatal perceived stress, in a primary and replication cohort. Prenatal BA estimates from early pregnancy explained variance in gestational age at birth above and beyond the influence of other recognized preterm birth risk factors. Sensitivity analyses indicated that this signal was driven primarily by self-identified African American participants. This predictive relationship was sensitive to small variations in the BA estimation algorithm. Benefits and limitations of using BA in translational research and clinical applications for preterm birth are considered. [ABSTRACT FROM AUTHOR]

Depression onset during and after pregnancy is prevalent and associated with significant implications for maternal, child, and family health. Although environmental risk factors important to the expression of pregnancy-related depression are well known, knowledge of the genetic underpinning is limited. Given the joint contribution of environmental and genetic factors to depression risk liability, DNA methylation presents itself as an ideal biomarker to investigate basic mechanisms and opportunities for translational research to care for pregnancy-related depression health outcomes. This article is an introduction to DNA methylation and its potential to serve as a marker of depression risk during pregnancy and the postpartum. This commentary discusses current clinical uses of DNA methylation–based testing and how it may be applied to perinatal depression clinical care and management. [ABSTRACT FROM AUTHOR]