Affective Instability, Childhood Trauma and Major Affective Disorders

BACKGROUND: Affective instability (AI), childhood trauma, and mental illness are linked, but evidence in affective disorders is limited, despite both AI and childhood trauma being associated with poorer outcomes. Aims were to compare AI levels in bipolar disorder I (BPI) and II (BPII), and major depressive disorder recurrent (MDDR), and to examine the association of AI and childhood trauma within each diagnostic group. METHODS: AI, measured using the Affective Lability Scale (ALS), was compared between people with DSM-IV BPI (n=923), BPII (n=363) and MDDR (n=207) accounting for confounders and current mood. Regression modelling was used to examine the association between AI and childhood traumas in each diagnostic group. RESULTS: ALS scores in descending order were BPII, BPI, MDDR, and differences between groups were significant (p<0.05). Within the BPI group any childhood abuse (p=0.021), childhood physical abuse (p=0.003) and the death of a close friend in childhood (p=0.002) were significantly associated with higher ALS score but no association was found between childhood trauma and AI in BPII and MDDR. LIMITATIONS: The ALS is a self-report scale and is subject to retrospective recall bias. CONCLUSIONS: AI is an important dimension in bipolar disorder independent of current mood state. There is a strong link between childhood traumatic events and AI levels in BPI and this may be one way in which exposure and disorder are linked. Clinical interventions targeting AI in people who have suffered significant childhood trauma could potentially change the clinical course of bipolar disorder.

Affective Instability, Childhood Trauma and Major Affective Disorders

Background and Aims Affective instability (AI), childhood trauma, and mental illness are linked, but evidence in affective disorders is limited, despite both AI and childhood trauma being associated with poorer outcomes. Aims were to compare AI levels in bipolar disorder I (BPI) and II (BPII), and major depressive disorder recurrent (MDDR), and to examine the association of AI and childhood trauma within each diagnostic group. Methods AI, measured using the Affective Lability Scale (ALS), was compared between people with DSM-IV BPI (n = 923), BPII (n = 363) and MDDR (n = 207) accounting for confounders and current mood. Regression modelling was used to examine the association between AI and childhood traumas in each diagnostic group. Results ALS scores in descending order were BPII, BPI, MDDR, and differences between groups were significant (p < 0.05). Within the BPI group any childhood abuse (p = 0.021), childhood physical abuse (p = 0.003) and the death of a close friend in childhood (p = 0.002) were significantly associated with higher ALS score but no association was found between childhood trauma and AI in BPII and MDDR. Conclusions AI is an important dimension in bipolar disorder independent of current mood state. There is a strong link between childhood traumatic events and AI levels in BPI and this may be one way in which exposure and disorder are linked. Clinical interventions targeting AI in people who have suffered significant childhood trauma could potentially change the clinical course of bipolar disorder.

Polygenic Interactions with Environmental Adversity in the Aetiology of Major Depressive Disorder

Major depressive disorder (MDD) is a common and disabling condition with well-established heritability and environmental risk factors. Gene–environment interaction studies in MDD have typically investigated candidate genes, though the disorder is known to be highly polygenic. This study aims to test for interaction between polygenic risk and stressful life events (SLEs) or childhood trauma (CT) in the aetiology of MDD. The RADIANT UK sample consists of 1605 MDD cases and 1064 controls with SLE data, and a subset of 240 cases and 272 controls with CT data. Polygenic risk scores (PRS) were constructed using results from a mega-analysis on MDD by the Psychiatric Genomics Consortium. PRS and environmental factors were tested for association with case/control status and for interaction between them. PRS significantly predicted depression, explaining 1.1% of variance in phenotype (p = 1.9 × 10−6). SLEs and CT were also associated with MDD status (p = 2.19 × 10−4 and p = 5.12 × 10−20, respectively). No interactions were found between PRS and SLEs. Significant PRSxCT interactions were found (p = 0.002), but showed an inverse association with MDD status, as cases who experienced more severe CT tended to have a lower PRS than other cases or controls. This relationship between PRS and CT was not observed in independent replication samples. CT is a strong risk factor for MDD but may have greater effect in individuals with lower genetic liability for the disorder. Including environmental risk along with genetics is important in studying the aetiology of MDD and PRS provide a useful approach to investigating gene–environment interactions in complex traits.