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Several social determinants of health (SDoH) have been associated with the onset of major depressive disorder (MDD). However, prior studies largely focused on individual SDoH and thus less is known about the relative importance (RI) of SDoH variables, especially in older adults. Given that risk factors for MDD may differ across the lifespan, we aimed to identify the SDoH that was most strongly related to newly diagnosed MDD in a cohort of older adults.
We used self-reported health-related survey data from 41 174 older adults (50–89 years, median age = 67 years) who participated in the Mayo Clinic Biobank, and linked ICD codes for MDD in the participants' electronic health records. Participants with a history of clinically documented or self-reported MDD prior to survey completion were excluded from analysis (N = 10 938, 27%). We used Cox proportional hazards models with a gradient boosting machine approach to quantify the RI of 30 pre-selected SDoH variables on the risk of future MDD diagnosis.
Following biobank enrollment, 2073 older participants were diagnosed with MDD during the follow-up period (median duration = 6.7 years). The most influential SDoH was perceived level of social activity (RI = 0.17). Lower level of social activity was associated with a higher risk of MDD [hazard ratio = 2.27 (95% CI 2.00–2.50) for highest v. lowest level].
Across a range of SDoH variables, perceived level of social activity is most strongly related to MDD in older adults. Monitoring changes in the level of social activity may help identify older adults at an increased risk of MDD.
Prospective studies are needed to assess the influence of pre-pandemic risk factors on mental health outcomes following the COVID-19 pandemic. From direct interviews prior to (T1), and then in the same individuals after the pandemic onset (T2), we assessed the influence of personal psychiatric history on changes in symptoms and wellbeing.
Two hundred and four (19–69 years/117 female) individuals from a multigenerational family study were followed clinically up to T1. Psychiatric symptom changes (T1-to-T2), their association with lifetime psychiatric history (no, only-past, and recent psychiatric history), and pandemic-specific worries were investigated.
At T2 relative to T1, participants with recent psychopathology (in the last 2 years) had significantly fewer depressive (mean, M = 41.7 v. 47.6) and traumatic symptoms (M = 6.6 v. 8.1, p < 0.001), while those with no and only-past psychiatric history had decreased wellbeing (M = 22.6 v. 25.0, p < 0.01). Three pandemic-related worry factors were identified: Illness/death, Financial, and Social isolation. Individuals with recent psychiatric history had greater Illness/death and Financial worries than the no/only-past groups, but these worries were unrelated to depression at T2. Among individuals with no/only-past history, Illness/death worries predicted increased T2 depression [B = 0.6(0.3), p < 0.05].
As recent psychiatric history was not associated with increased depression or anxiety during the pandemic, new groups of previously unaffected persons might contribute to the increased pandemic-related depression and anxiety rates reported. These individuals likely represent incident cases that are first detected in primary care and other non-specialty clinical settings. Such settings may be useful for monitoring future illness among newly at-risk individuals.
In this three-generation longitudinal study of familial depression, we investigated the continuity of parenting styles, and major depressive disorder (MDD), temperament, and social support during childrearing as potential mechanisms. Each generation independently completed the Parental Bonding Instrument (PBI), measuring individuals’ experiences of care and overprotection received from parents during childhood. MDD was assessed prospectively, up to 38 years, using the semi-structured Schedule for Affective Disorders and Schizophrenia (SADS). Social support and temperament were assessed using the Social Adjustment Scale – Self-Report (SAS-SR) and Dimensions of Temperament Scales – Revised, respectively. We first assessed transmission of parenting styles in the generation 1 to generation 2 cycle (G1→G2), including 133 G1 and their 229 G2 children (367 pairs), and found continuity of both care and overprotection. G1 MDD accounted for the association between G1→G2 experiences of care, and G1 social support and temperament moderated the transmission of overprotection. The findings were largely similar when examining these psychosocial mechanisms in 111 G2 and their spouses (G2+S) and their 136 children (G3) (a total of 223 pairs). Finally, in a subsample of families with three successive generations (G1→G2→G3), G2 experiences of overprotection accounted for the association between G1→G3 experiences of overprotection. The results of this study highlight the roles of MDD, temperament, and social support in the intergenerational continuity of parenting, which should be considered in interventions to “break the cycle” of poor parenting practices across generations.
ABSTRACT IMPACT: Lipidomics is emerging as a powerful strategy to identify biomarkers for Major Depressive Disorder, as well as therapeutic targets in lipid metabolic pathways. OBJECTIVES/GOALS: Lipidomics is increasingly recognized in precision psychiatry for global lipid perturbations in patients suffering from Major Depressive Disorder (MDD). We will test the hypothesis that lipid metabolism dysregulation is associated with familial risk of depression. METHODS/STUDY POPULATION: Patients with MDD (G1), children (G2), and grandchildren (G3) have been part of a longitudinal study since 1982. If a parent G2 and grandparent G1 have MDD, G3 is considered a high risk of depression. Biospecimens (saliva and serum) were collected for full exome sequencing and RNA analysis. Samples will also be extracted for lipid content and lipids will be identified by mass spectrometry. A panel of nearly 600 lipid species can reliably be identified and quantified using liquid chromatography paired with tandem mass spectrometry (LC-MS/MS). Dysregulated lipids will be correlated with familial risk of depression in samples of G3. RESULTS/ANTICIPATED RESULTS: We hypothesize that dysregulation of lipids and lipid metabolism will be apparent in biospecimens from the high risk compared to the low risk of depression. Also, alterations in RNA transcriptomics of genes involved in lipid metabolic networks are associated with familial risk of depression. Several differential lipid species were previously identified to be associated with MDD. Reduced phosphatidylcholine(PC), phosphatidylethanolamine(PE), phosphatidylinositol(PI), and increased LysoPC, LysoPE, ceramide, triacylglycerol, and diacylglycerol levels have been correlated to MDD. However, these results need to be replicated in independent studies using lipidomics analysis. DISCUSSION/SIGNIFICANCE OF FINDINGS: It is highly likely that completely novel cellular targets will emerge from these studies by uncovering the convergence of lipidomics and genetic variance of lipid metabolic enzymes as biomarkers for predisposition to MDD as well as potential targets for therapeutic development for MDD.
This chapter outlines methods for studying the genetic basis of psychiatric disorders. The two types of methods, single nucleotide polymorphism (SNP) genotyping and sequencing, are focused on characterizing two different types of variants predisposing to disease: common and rare. The chapter provides a technical overview of two technologies that have been used in the majority of linkage studies and population-based genome-wide association studies (GWAS): Illumina BeadArray Mapping arrays and Affymertrix Gene-Chip Arrays. While there are many potential applications of these technologies, the chapter focuses on those involving sequencing DNA for the purpose of identifying the genetic basis of disease, specifically dividing this section into targeted methods and genome-wide approaches. The chapter provides an overview of features for all three commercially available platforms (SOLiD, GA and 454). Analysis of next-generation sequencing data falls into three categories: alignment/assembly; quality control; and variant calling.
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