Prenatal Depressive Symptoms, Self-Rated Health, and Diabetes Self-Efficacy: A Moderated Mediation Analysis

Mediation analysis in longitudinal study with high-dimensional methylation mediators

Mediation analysis has been widely utilized to identify potential pathways connecting exposures and outcomes. However, there remains a lack of analytical methods for high-dimensional mediation analysis in longitudinal data. To tackle this concern, we proposed an effective and novel approach with variable selection and the indirect effect (IE) assessment based on both linear mixed-effect model and generalized estimating equation. Initially, we employ sure independence screening to reduce the dimension of candidate mediators. Subsequently, we implement the Sobel test with the Bonferroni correction for IE hypothesis testing. Through extensive simulation studies, we demonstrate the performance of our proposed procedure with a higher F$_{1}$ score (0.8056 and 0.9983 at sample sizes of 150 and 500, respectively) compared with the linear method (0.7779 and 0.9642 at the same sample sizes), along with more accurate parameter estimation and a significantly lower false discovery rate. Moreover, we apply our methodology to explore the mediation mechanisms involving over 730 000 DNA methylation sites with potential effects between the paternal body mass index (BMI) and offspring growing BMI in the Shanghai sleeping birth cohort data, leading to the identification of two previously undiscovered mediating CpG sites.

Assessing the risk of prenatal depressive symptoms in Chinese women: an integrated evaluation of serum metabolome, multivitamin supplement intake, and clinical blood indicators

Background

Prenatal depressive symptoms (PDS) is a serious public health problem. This study aimed to develop an integrated panel and nomogram to assess at-risk populations by examining the association of PDS with the serum metabolome, multivitamin supplement intake, and clinical blood indicators.

Methods

This study comprised 221 pregnant women, categorized into PDS and non-PDS groups based on the Edinburgh postnatal depression scale. The participants were divided into training and test sets according to their enrollment time. We conducted logistic regression analysis to identify risk factors, and employed liquid chromatography/high resolution mass spectrometry-based serum metabolome analysis to identify metabolic biomarkers. Multiple factor analysis was used to combine risk factors, clinical blood indicators and key metabolites, and then a nomogram was developed to estimate the probability of PDS.

Results

We identified 36 important differential serum metabolites as PDS biomarkers, mainly involved in amino acid metabolism and lipid metabolism. Multivitamin intake works as a protective factor for PDS. The nomogram model, including multivitamin intake, HDL-C and three key metabolites (histidine, estrone and valylasparagine), exhibited an AUC of 0.855 in the training set and 0.774 in the test set, and the calibration curves showed good agreement, indicating that the model had good stability.

Conclusion

Our approach integrates multiple models to identify metabolic biomarkers for PDS, ensuring their robustness. Furthermore, the inclusion of dietary factors and clinical blood indicators allows for a comprehensive characterization of each participant. The analysis culminated in an intuitive nomogram based on multimodal data, displaying potential performance in initial PDS risk assessment.

People with Diabetes Have Poorer Self-Rated Health (SRH) and Diabetes Moderates the Association between Age and SRH

Diabetes is a severe chronic condition that is related to decreased physical functioning. Recently, there has been growing interest in understanding how a brief report on health such as self-rated health (SRH) could be used to track changes in health status and service needs in people with diabetes. The current research aims to investigate how SRH is affected by diabetes and how diabetes could moderate the association between age and SRH. By analyzing data from 47,507 participants, with 2869 of them clinically diagnosed with diabetes, the current study found that people with diabetes had significantly poorer SRH than people without diabetes after controlling for demographic covariates (t(2868) = -45.73, p < 0.001, 95% C.I. (-0.92, -0.85), Cohen's d = -0.85). In addition, diabetes was a significant moderator of the relationship between age and SRH (b = 0.01, p < 0.001, 95% C.I. (0.01, 0.01)). Specifically, age was more strongly related to SRH in people without diabetes (b = -0.015, p < 0.001, 95% C.I. (-0.016, -0.015)) than in people with diabetes (b = -0.007, p < 0.001, 95% C.I. (-0.010, -0.004)). Health professionals should aim to improve SRH in people with diabetes given that SRH is related to various outcomes.