Shifting the paradigm of research-to-policy impact: Infrastructure for improving researcher engagement and collective action.

The body of scientific knowledge accumulated by the scholarly disciplines such as Developmental Psychopathology can achieve meaningful public impact if wielded and used in policy decision-making. Scientific study of how policymakers use research evidence underscores the need for researchers' policy engagement; however, barriers in the academy create conditions in which there is a need for infrastructure that increases the feasibility of researchers' partnership with policymakers. This need led to the development of the Research-to-Policy Collaboration model, a systematic approach for developing "boundary spanning" infrastructure, which has been experimentally tested and shown to improve policymakers' use of research evidence and bolster researchers' policy skills and engagement. This paper presents original research regarding the optimization of the RPC model, which sought to better serve and engage scholars across the globe. Trial findings shed light on ways to improve conditions that make good use of researchers' time for policy engagement via a virtual platform and enhanced e-communications. Future directions, implications, and practical guidelines for how scientists can engage in the political process and improve the impact of a collective discipline are also discussed.

Longitudinal method comparison: modeling polygenic risk for post-traumatic stress disorder over time in individuals of African and European ancestry.

Cross-sectional data allow the investigation of how genetics influence health at a single time point, but to understand how the genome impacts phenotype development, one must use repeated measures data. Ignoring the dependency inherent in repeated measures can exacerbate false positives and requires the utilization of methods other than general or generalized linear models. Many methods can accommodate longitudinal data, including the commonly used linear mixed model and generalized estimating equation, as well as the less popular fixed-effects model, cluster-robust standard error adjustment, and aggregate regression. We simulated longitudinal data and applied these five methods alongside naïve linear regression, which ignored the dependency and served as a baseline, to compare their power, false positive rate, estimation accuracy, and precision. The results showed that the naïve linear regression and fixed-effects models incurred high false positive rates when analyzing a predictor that is fixed over time, making them unviable for studying time-invariant genetic effects. The linear mixed models maintained low false positive rates and unbiased estimation. The generalized estimating equation was similar to the former in terms of power and estimation, but it had increased false positives when the sample size was low, as did cluster-robust standard error adjustment. Aggregate regression produced biased estimates when predictor effects varied over time. To show how the method choice affects downstream results, we performed longitudinal analyses in an adolescent cohort of African and European ancestry. We examined how developing post-traumatic stress symptoms were predicted by polygenic risk, traumatic events, exposure to sexual abuse, and income using four approaches-linear mixed models, generalized estimating equations, cluster-robust standard error adjustment, and aggregate regression. While the directions of effect were generally consistent, coefficient magnitudes and statistical significance differed across methods. Our in-depth comparison of longitudinal methods showed that linear mixed models and generalized estimating equations were applicable in most scenarios requiring longitudinal modeling, but no approach produced identical results even if fit to the same data. Since result discrepancies can result from methodological choices, it is crucial that researchers determine their model a priori, refrain from testing multiple approaches to obtain favorable results, and utilize as similar as possible methods when seeking to replicate results.

Cross-tissue comparison of telomere length and quality metrics of DNA among individuals aged 8 to 70 years.

Telomere length (TL) is an important biomarker of cellular aging, yet its links with health outcomes may be complicated by use of different tissues. We evaluated within- and between-individual variability in TL and quality metrics of DNA across five tissues using a cross-sectional dataset ranging from 8 to 70 years (N = 197). DNA was extracted from all tissue cells using the Gentra Puregene DNA Extraction Kit. Absolute TL (aTL) in kilobase pairs was measured in buccal epithelial cells, saliva, dried blood spots (DBS), buffy coat, and peripheral blood mononuclear cells (PBMCs) using qPCR. aTL significantly shortened with age for all tissues except saliva and buffy coat, although buffy coat was available for a restricted age range (8 to 15 years). aTL did not significantly differ across blood-based tissues (DBS, buffy coat, PBMC), which had significantly longer aTL than buccal cells and saliva. Additionally, aTL was significantly correlated for the majority of tissue pairs, with partial Spearman's correlations controlling for age and sex ranging from ⍴ = 0.18 to 0.51. We also measured quality metrics of DNA including integrity, purity, and quantity of extracted DNA from all tissues and explored whether controlling for DNA metrics improved predictions of aTL. We found significant tissue variation: DNA from blood-based tissues had high DNA integrity, more acceptable A260/280 and A260/230 values, and greater extracted DNA concentrations compared to buccal cells and saliva. Longer aTL was associated with lower DNA integrity, higher extracted DNA concentrations, and higher A260/230, particularly for saliva. Model comparisons suggested that incorporation of quality DNA metrics improves models of TL, although relevant metrics vary by tissue. These findings highlight the merits of using blood-based tissues and suggest that incorporation of quality DNA metrics as control variables in population-based studies can improve TL predictions, especially for more variable tissues like buccal and saliva.

The Association Between a Neighborhood Adverse Childhood Experiences Index and Body Mass Index Among New York City Youth.

Background: The role of neighborhood factors in the association between adverse childhood experiences (ACEs) and body mass index (BMI) has not been widely studied. A neighborhood ACEs index (NAI) captures neighborhood environment factors associated with ACE exposure. This study examined associations between BMI and an NAI among New York City (NYC) youth. An exploratory objective examined the NAI geographic distribution across NYC neighborhoods. Methods: Data for students attending NYC public general education schools in kindergarten-12th grade from 2006-2017 (n = 1,753,867) were linked to 25 geospatial datasets capturing neighborhood characteristics for every census tract in NYC. Multivariable hierarchical linear regression tested associations between BMI and the NAI; analyses also were conducted by young (<8 years), school age (8-12 years), and adolescent (>12 years) subgroups. In addition, NAI was mapped by census tract, and local Moran's I identified clusters of high and low NAI neighborhoods. Results: Higher BMI was associated with higher NAI across all sex and age groups, with largest magnitude of associations for girls (medium NAI vs. low NAI: unstandardized ß = 0.112 (SE 0.008), standardized ß [effect size]=0.097, p < 0.001; high NAI vs. low NAI: unstandardized ß = 0.195 (SE 0.008), standardized ß = 0.178, p < 0.001) and adolescents (medium NAI vs. low NAI: unstandardized ß = 0.189 (SE 0.014), standardized ß = 0.161, p < 0.001, high NAI vs. low NAI: unstandardized ß = 0.364 (SE 0.015), standardized ß = 0.334, p < 0.001 for adolescent girls; medium NAI vs. low NAI: unstandardized ß = 0.122 (SE 0.014), standardized ß = 0.095, p < 0.001, high NAI vs. low NAI: unstandardized ß = 0.217 (SE 0.015), standardized ß = 0.187, p < 0.001 for adolescent boys). Each borough of NYC included clusters of neighborhoods with higher and lower NAI exposure, although clusters varied in size and patterns of geographic dispersion across boroughs. Conclusions: A spatial index capturing neighborhood environment factors associated with ACE exposure is associated with higher BMI among NYC youth. Findings complement prior literature about relationships between neighborhood environment and obesity risk, existing research documenting ACE-obesity associations, and the potential for neighborhood factors to be a source of adversity. Collectively, evidence suggests that trauma-informed place-based obesity reduction efforts merit further exploration as potential means to interrupt ACE-obesity associations.

Cross-Tissue Comparison of Epigenetic Aging Clocks in Humans.

Epigenetic clocks are a common group of tools used to measure biological aging - the progressive deterioration of cells, tissues and organs. Epigenetic clocks have been trained almost exclusively using blood-based tissues but there is growing interest in estimating epigenetic age using less-invasive oral-based tissues (i.e., buccal or saliva) in both research and commercial settings. However, differentiated cell types across body tissues exhibit unique DNA methylation landscapes and age-related alterations to the DNA methylome. Applying epigenetic clocks derived from blood-based tissues to estimate epigenetic age of oral-based tissues may introduce biases. We tested the within-person comparability of common epigenetic clocks across five tissue types: buccal epithelial, saliva, dry blood spots, buffy coat (i.e., leukocytes), and peripheral blood mononuclear cells. We tested 284 distinct tissue samples from 83 individuals aged 9-70 years. Overall, there were significant within-person differences in epigenetic clock estimates from oral-based versus blood-based tissues, with average differences of almost 30 years observed in some age clocks. In addition, most epigenetic clock estimates of blood-based tissues exhibited low correlation with estimates from oral-based tissues despite controlling for cellular proportions and other technical factors. Our findings indicate that application of blood-derived epigenetic clocks in oral-based tissues may not yield comparable estimates of epigenetic age, highlighting the need for careful consideration of tissue type when estimating epigenetic age.