An epigenetic clock for gestational age based on human umbilical vein endothelial cells from a diverse population of newborns.

Background: Epigenetic clocks are emerging as a useful tool in many areas of research. Many epigenetic clocks have been developed for adults; however, there are fewer clocks focused on newborns and most are trained using blood from European ancestry populations. In this study, we built an epigenetic clock based on primary human umbilical vein endothelial cells from a racially and ethnically diverse population. Results: Using human umbilical vein endothelial cell [HUVEC]-derived DNA, we calculated epigenetic gestational age using 83 CpG sites selected through elastic net regression. In this study with newborns from different racial/ethnic identities, epigenetic gestational age and clinical gestational age were more highly correlated (r = 0.85), than epigenetic clocks built from adult and other pediatric populations. The correlation was also higher than clocks based on blood samples from newborns with European ancestry. We also found that birth weight was positively associated with epigenetic gestational age acceleration (EGAA), while NICU admission was associated with lower EGAA. Newborns self-identified as Hispanic or non-Hispanic Black had lower EGAA than self-identified as non-Hispanic White. Conclusions: Epigenetic gestational age can be used to estimate clinical gestational age and may help index neonatal development. Caution should be exercised when using epigenetic clocks built from adults with children, especially newborns. We highlight the importance of cell type-specific epigenetic clocks and general pan tissue epigenetic clocks derived from a large racially and ethnically diverse population.

Preanalytic and Analytic Quality System Considerations in Noncoding RNA Biomarker Development for Clinical Diagnostics.

A frequent topic of biomedical research is the potential clinical use of non-coding (nc) RNAs as quantitative biomarkers for a broad spectrum of health and disease. However, ncRNA analyses have not been pressed into widespread diagnostic use. Strong preclinical evidence suggests obstacles in the translation and reproducibility of this type of biomarker which may result from preanalytical and analytical variation in the non-standardized processes used to collect, process, and store samples, as well as the substantive differences between small and long ncRNA. We performed a narrative review of selected literature, through the lens of key laboratory-developed test (LDT) regulations under the Clinical Laboratory Improvement Amendments (CLIA) in the United States, to study critical gaps in ncRNA validation studies. This review describes the leading candidate ncRNA subclasses, their biogenesis and cellular function, and identifies specific pre-analytical variables with disproportionate impact on testing performance. We summarize these findings with strategic recommendations to clinicians and biomedical scientists involved in the design, conduct, and translation of ncRNA biomarker development.

Defining the Healthy Infant Metabolome: Liquid Chromatography Tandem-Mass Spectrometry Analysis of Dried Blood Spot Extracts from the Prospective Research on Early Determinants of Illness and Children's Health Trajectories Birth Cohort Study.

Newborn screening using dried plasma spots offers preanalytical advantages over conventional cards for plasma-associated targets of interest. Herein we present dried plasma spot-based methods for measuring metabolites using a 250+ compound liquid chromatography tandem mass spectrometry library. Quality assurance reduced this library to 134, and from these, 30 compounds determined the normal newborn reference ranges.

Use of artificial neural networks to determine cognitive impairment and therapeutic effectiveness in Alzheimer's transgenic mice.

Behavioral testing of transgenic mouse models of Alzheimer's disease (AD) is the functional endpoint for determining the effectiveness of therapeutic interventions and elucidating AD pathogenesis. Utilizing these mouse models, there have been remarkably few attempts to analyze multiple behavioral measures/tasks with higher-level computation techniques, either to distinguish performance between transgenic groups or to reveal any "overall" cognitive benefit of a given therapeutic. The present study compared the classificatory accuracy of artificial neural networks (ANNs) versus more traditional discriminant function analysis (DFA) using multiple behavioral measures/tasks from two AD transgenic mouse investigations. These investigations were to determine if AD transgenic mice could be cognitively-protected by either long-term caffeine administration (CA) or by a cognitively-stimulating environment (SE). Both the entire set of behavioral measures and a subset of 8 cognitive-based measures were analyzed. Both classifiers revealed a beneficial "overall" effect of CA and SE to protect AD transgenic mice across multiple cognitive measures/tasks. However, for both CA and SE datasets, the ANN was superior to DFA for discerning transgenicity (non-transgenic vs. transgenic-controls) across multiple behavioral measures. These results indicate that ANNs have an excellent capacity to discriminate cognitive impairment in AD transgenic mice and thus designate ANNs as a novel, sensitive method for cognitive assessment in Alzheimer's research.