Yu Jiang

Social determinants of health (SDOH) may influence hospital readmissions. The purpose of this project was to apply provider-assigned International Classification of Diseases-10-CM codes to the Healthy People 2030 SDOH Framework for use in future research focused on hospital readmissions using the 2018 Nationwide Readmissions Database (NRD). We classified all subcodes codes ranging from Z55 to Z65 that were captured within the 2018 NRD. Three researchers independently categorized 80 unique codes into the 5 Healthy People 2030 domains. Inter-rater reliability was 0.664 and the Kappa statistic was 0.54 for initial classifications. The Kappa statistic shows moderate agreement between the raters. After robust discussion, we adjusted 33 classifications to reach classification consensus. Among the 80 codes classified, 8 (10%) were classified into Economic Stability, 6 (7.5%) into Education Access and Quality, 0 into Healthcare Access and Quality, and 13 (16.3%) into Neighborhood and Built Environment, and 53 (66.3%) into Social and Community Context. Classification challenges were due to overlapping and related features of the descriptions that could be applied to more than one domain. Clear coding practice will support researchers, case managers, social workers, and providers in partnering with patients and their family to negotiate a safe evidence-based discharge transition plan.

Mediation analysis is commonly implemented in psychological, epidemiological, and social behavior studies to identify potential factors that mediate associations between exposures and physical or psychological outcomes. Various analytical tools are available to perform mediation analyses, among which Mplus is widely used due to its user-friendly interface. In practice, sumptuous results provided by Mplus, such as the estimated standardized and unstandardized effect sizes, can be difficult for researchers to choose to match their studies. Through a comprehensive review and utilizing findings from a proven study, we proposed guidelines and recommendations to help users select between standardized or unstandardized results based on data attributes and users’ hypotheses. We also provided guidelines to choose from several types of standardized values based on the types of variables, including exposures, mediators, and outcomes.

Early patterning of DNA methylation (DNAm) may play an important role in later disease development. To better understand intergenerational epigenetic inheritance, we investigated the correlation between DNAm in blood in mother-newborn and in father-newborn pairs in the Isle of Wight (IoW) birth cohort. For parent-newborn pairs (n = 48), offspring DNAm was measured in cord blood and the parent’s DNAm in whole blood. Mothers’ DNAm was analyzed at birth (Guthrie card), age 18, early and late pregnancy respectively, and fathers’ DNAm was measured during the mother’s pregnancy. Linear regressions were applied to assess the intergenerational correlation of parental DNAm with that of offspring. Among various pairs of mother-newborn and father-newborn DNAm, the pairs where the mothers’ DNAm was measured at age 18 years exhibited the highest number of CpGs with significant intergenerational correlation in DNAm, with 1829 CpGs (0.54%) of the 338,526 CpGs studied (FDR < 0.05). Amongst these 1829 CpGs, 986 (54%) are known quantitative trait loci (QTL) for CpG methylation (methQTL). When the mother’s DNAm was assessed at early pregnancy, the number of CpGs showing intergenerational correlation was the smallest (384 CpGs, 0.11%). The second smallest number of such CpGs (559 CpGs, 0.17%) was found when investigating DNAm in offspring cord blood and father pairs. The low proportions of intergenerationally correlated CpGs suggest that epigenetic inheritance is limited.

The association between newborn DNA methylation (DNAm) and asthma acquisition (AA) during adolescence has been suggested. Lung function (LF) has been shown to be associated with asthma risk and its severity. However, the role of LF in the associations between DNAm and AA is unclear, and it is also unknown whether the association between DNAm and AA is consistent with that between DNAm and LF. We address this question through assessing newborn epigenetic features of preadolescence LF and of AA during adolescence, along with their biological pathways and processes. Our study’s primary medical significance lies in advancing the understanding of asthma’s early life origins. By investigating epigenetic markers in newborns and their association with lung function in preadolescence, we aim to uncover potential early biomarkers of asthma risk. This could facilitate earlier detection and intervention strategies. Additionally, exploring biological pathways linking early lung function to later asthma development can offer insights into the disease’s pathogenesis, potentially leading to novel therapeutic targets. Methods: The study was based on the Isle of Wight Birth cohort (IOWBC). Female subjects with DNAm data at birth and with no asthma at age 10 years were included (n = 249). The R package ttScreening was applied to identify CpGs potentially associated with AA from 10 to 18 years and with LF at age 10 (FEV1, FVC, and FEV1/FVC), respectively. Agreement in identified CpGs between AA and LF was examined, along with their biological pathways and processes via the R function gometh. We tested the findings in an independent cohort, the Avon Longitudinal Study of Parents and Children (ALSPAC), to examine overall replicability. Results: In IOWBC, 292 CpGs were detected with DNAm associated with AA and 1517 unique CpGs for LF (514 for FEV1, 436 for FVC, 408 for FEV1/FVC), with one overlapping CpG, cg23642632 (NCKAP1) between AA and LF. Among the IOWBC-identified CpGs, we further tested in ALSPAC and observed the highest agreement between the two cohorts in FVC with respect to the direction of association and statistical significance. Epigenetic enrichment analyses indicated non-specific connections in the biological pathways and processes between AA and LF. Conclusions: The present study suggests that FEV1, FVC, and FEV1/FVC (as objective measures of LF) and AA (incidence of asthma) are likely to have their own specific epigenetic features and biological pathways at birth. More replications are desirable to fully understand the complexity between DNAm, lung function, and asthma acquisition.

Maternal smoking in pregnancy (MSP) affects the offspring’s DNA methylation (DNAm). There is a lack of knowledge regarding individual differences in susceptibility to exposure to MSP. Glutathione S-transferase (GST) genes are involved in protection against harmful oxidants such as those found in cigarette smoke. This study aimed to test whether polymorphisms in GST genes influence the effect of MSP on offspring DNAm. Using data from the Isle of Wight birth cohort, we assessed the association of MSP and offspring DNAm in 493 mother-child dyads (251 male, 242 female) with the effect-modifying role of GST gene polymorphism (at rs506008, rs574344, rs12736389, rs3768490, rs1537234, and rs1695). MSP was assessed by levels of nicotine and its downstream metabolites (cotinine, norcotinine, and hydroxycotinine) in maternal sera. In males, associations of hydroxycotinine with DNAm at cg18473733, cg25949550, cg11647108, and cg01952185 and norcotinine with DNAm at cg09935388 were modified by GST gene polymorphisms (p-values < 0.05). In females, associations of hydroxycotinine with DNAm at cg12160087 and norcotinine with DNAm at cg18473733 were modified by GST gene polymorphisms (p-values < 0.05). Our study emphasizes the role of genetic polymorphism in GST genes in DNAm’s susceptibility to MSP.

Background: Given increasing use of e-cigarettes among adolescents in the United States and its potential for nicotine addiction, encouraging adolescents to quit using these products has become a public health priority. This study examined factors at various socio-ecological levels associated with e-cigarette quit intention (pre-contemplation, contemplation, or preparation) using the Stages of Change of the Trans-theoretical Model among the U.S. adolescents. Methods: We used cross-sectional data from the past 30-day adolescent exclusive e-cigarette users participating in Wave 4 of the Population Assessment of Tobacco and Health study (n = 349). Weighted adjusted multinomial logistic regression models were used to analyze the data. Results: Compared to pre-contemplators and contemplators, preparators were more likely to believe that nicotine in e-cigarettes was “very/extremely harmful” (vs. “not at all harmful”) to health (p < 0.001) and people cause a “lot of harm” (vs. “no harm”) to themselves when they use e-cigarettes (p < 0.001). In comparison to pre-contemplators, contemplators and preparators were more likely to report that their parents/guardians talked with them about not using e-cigarettes than those whose parents/guardians did not talk with them (p < 0.001). Additionally, contemplators and preparators were also more likely to report that they “often/very often” (vs. never) noticed health warnings on e-cigarette packages (p < 0.001). Conclusion: Our findings suggest that harm perception, influence of family, and e-cigarette health warnings are some of the important factors associated with the stages of change for intention to quit among adolescent e-cigarette users. This study will help public health practitioners and researchers design multi-level e-cigarette cessation interventions for adolescents.