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Challenges in Analyzing Functional Epigenetic Data in Perspective of Adolescent Psychiatric Health.

ABSTRACT: The formative period of adolescence plays a crucial role in the development of skills and abilities for adulthood. Adolescents who are affected by mental health conditions are at risk of suicide and social and academic impairments. Gene-environment complementary contributions to the molecular mechanisms involved in psychiatric disorders have emphasized the need to analyze epigenetic marks such as DNA methylation (DNAm) and non-coding RNAs. However, the large and diverse bioinformatic and statistical methods, referring to the confounders of the statistical models, application of multiple-testing adjustment methods, questions regarding the correlation of DNAm across tissues, and sex-dependent differences in results, have raised challenges regarding the interpretation of the results. Based on the example of generalized anxiety disorder (GAD) and depressive disorder (MDD), we shed light on the current knowledge and usage of methodological tools in analyzing epigenetics. Statistical robustness is an essential prerequisite for a better understanding and interpretation of epigenetic modifications and helps to find novel targets for personalized therapeutics in psychiatric diseases.

SUBMITTER: Manu DM

PROVIDER: S-EPMC9147258 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Challenges in Analyzing Functional Epigenetic Data in Perspective of Adolescent Psychiatric Health.

Manu Diana M DM Mwinyi Jessica J Schiöth Helgi B HB

International journal of molecular sciences 20220523 10

The formative period of adolescence plays a crucial role in the development of skills and abilities for adulthood. Adolescents who are affected by mental health conditions are at risk of suicide and social and academic impairments. Gene-environment complementary contributions to the molecular mechanisms involved in psychiatric disorders have emphasized the need to analyze epigenetic marks such as DNA methylation (DNAm) and non-coding RNAs. However, the large and diverse bioinformatic and statist ...[more]

PMID: 35628666

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Project description:BackgroundPerinatal markers of prenatal development are associated with offspring psychiatric symptoms. However, there is little research investigating the specificity of perinatal markers for the development of specific disorders. This study aimed to explore if perinatal markers are specifically associated with adolescent substance use disorder (SUDs).MethodsAdolescent participants from two study centers, one for SUD patients (n = 196) and one for general psychopathology (n = 307), were recruited for participation. Since the SUD participants presented with a number of comorbid disorders, we performed a 1-on-1 matching procedure, based on age, gender, and specific pattern of comorbid disorders. This procedure resulted in n = 51 participants from each group. From all participants and their mothers we recorded perinatal markers (mode of birth, weeks of completed pregnancy, birth weight, Apgar score after 5 min) as well as intelligence quotient (IQ). The SUD sample additionally filled out the Youth Safe Report (YSR) as well as the PQ-16 and the DUDIT. We aimed to distinguish the two groups (SUD sample vs. general psychiatric sample) based on the perinatal variables via a logistic regression analysis. Additionally, linear regressions were performed for the total group and the subgroups to assess the relationship between perinatal variables and IQ, YSR, DUDIT and PQ-16.ResultsThe perinatal variables were not able to predict group membership (X2 [4] = 4.77, p = .312, Cox & Snell R² = 0.053). Odds ratios indicated a small increase in probability to belonging to the general psychiatric sample instead of the SUD sample if birth was completed via C-section. After Bonferroni-correction, the linear regression models showed no relation between perinatal markers and IQ (p = .60, R² = 0.068), YSR (p = .09, R² = 0.121), DUDIT (p = .65, R² = 0.020), and PQ-16 (p = .73, R² =0.021).ConclusionPerinatal markers were not able to distinguish SUD patients from patients with diverse psychopathologies. This pattern contradicts previous findings, perhaps because our chosen markers reflect general processes instead of specific mechanistic explanations. Future studies should take care to investigate specific prenatal markers and associate them with psychopathology on the symptom level.

Project description:BackgroundOnline health communities (OHCs) have become a major source of social support for people with health problems. Members of OHCs interact online with similar peers to seek, receive, and provide different types of social support, such as informational support, emotional support, and companionship. As active participations in an OHC are beneficial to both the OHC and its users, it is important to understand factors related to users' participations and predict user churn for user retention efforts.ObjectiveThis study aimed to analyze OHC users' Web-based interactions, reveal which types of social support activities are related to users' participation, and predict whether and when a user will churn from the OHC.MethodsWe collected a large-scale dataset from a popular OHC for cancer survivors. We used text mining techniques to decide what kinds of social support each post contained. We illustrated how we built text classifiers for 5 different social support categories: seeking informational support (SIS), providing informational support (PIS), seeking emotional support (SES), providing emotional support (PES), and companionship (COM). We conducted survival analysis to identify types of social support related to users' continued participation. Using supervised machine learning methods, we developed a predictive model for user churn.ResultsUsers' behaviors to PIS, SES, and COM had hazard ratios significantly lower than 1 (0.948, 0.972, and 0.919, respectively) and were indicative of continued participations in the OHC. The churn prediction model based on social support activities offers accurate predictions on whether and when a user will leave the OHC.ConclusionsDetecting different types of social support activities via text mining contributes to better understanding and prediction of users' participations in an OHC. The outcome of this study can help the management and design of a sustainable OHC via more proactive and effective user retention strategies.

Dataset Information

Challenges in Analyzing Functional Epigenetic Data in Perspective of Adolescent Psychiatric Health.

Publications

Challenges in Analyzing Functional Epigenetic Data in Perspective of Adolescent Psychiatric Health.

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