Risk factor profiles for depression following childbirth or a chronic disease diagnosis: case-control study

Assessing the validity of a self-reported clinical diagnosis of schizophrenia

The increasing availability of biobanks is changing the way individuals are identified for genomic research. This study assesses the validity of a self-reported clinical diagnosis of schizophrenia. The study included 1744 clinically-ascertained participants with schizophrenia or schizoaffective disorder depressed-type (SA-D) diagnosed by self-report and/or research interview and 1453 UK Biobank participants with self-reported and/or medical record diagnosis of schizophrenia or SA-D. Unaffected controls included a total of 501,837 participants. We assessed the positive predictive values (PPV) of self-reported clinical diagnoses against research interview and medical record diagnoses. Polygenic risk scores (PRS) and phenotypes relating to demographics, education and employment were compared across diagnostic groups. The variance explained (r2) in schizophrenia PRS for each diagnostic group was compared to samples in the Psychiatric Genomics Consortium (PGC). In the clinically-ascertained participants, the PPV of self-reported schizophrenia for a research diagnosis of schizophrenia was 0.70, which increased to 0.81 after expanding the research diagnosis to schizophrenia or SA-D. In UK Biobank, the PPV of self-reported schizophrenia for a medical record diagnosis was 0.74. Compared to participants who self-reported, participants with a clinically-ascertained research diagnosis were younger and more likely to have a high school qualification. Participants with a medical record diagnosis in UK Biobank were less likely to be employed or have a high school qualification than those who self-reported. Schizophrenia PRS did not differ between participants that had a diagnosis from self-report, research diagnosis or medical records. Polygenic liability r2, for all diagnosis definitions, fell within the distribution of PGC schizophrenia cohorts. Self-reported measures of schizophrenia are justified in genomic research to maximise sample size and reduce the burden of in-depth interviews on participants, although within sample validation of diagnoses is recommended.

Assessing the validity of a self-reported clinical diagnosis of schizophrenia

Background

Diagnoses in psychiatric research can be derived from various sources. This study assesses the validity of a self-reported clinical diagnosis of schizophrenia.

Methods

The study included 3,029 clinically ascertained participants with schizophrenia or psychotic disorders diagnosed by self-report and/or research interview and 1,453 UK Biobank participants with self-report and/or medical record diagnosis of schizophrenia or schizoaffective disorder depressed-type (SA-D). We assessed positive predictive values (PPV) of self-reported clinical diagnoses against research interview and medical record diagnoses. We compared polygenic risk scores (PRS) and phenotypes across diagnostic groups, and compared the variance explained by schizophrenia PRS to samples in the Psychiatric Genomics Consortium (PGC).

Results

In the clinically ascertained sample, the PPV of self-reported schizophrenia to a research diagnosis of schizophrenia was 0.70, which increased to 0.81 when benchmarked against schizophrenia or SA-D. In UK Biobank, the PPV of self-reported schizophrenia to a medical record diagnosis was 0.74. Compared to self-report participants, those with a research diagnosis were younger and more likely to have a high school qualification (clinically ascertained sample) and those with a medical record diagnosis were less likely to be employed or have a high school qualification (UK Biobank). Schizophrenia PRS did not differ between participants that had a diagnosis from self-report, research diagnosis or medical record diagnosis. Polygenic liability r2, for all diagnosis definitions, fell within the distribution of PGC schizophrenia cohorts.

Conclusions

Self-report measures of schizophrenia are justified in research to maximise sample size and representativeness, although within sample validation of diagnoses is recommended.

Childhood trauma and social support affect symptom profiles through cortical thickness abnormalities in major depressive disorder: A structural equation modeling analysis

BACKGROUND

Childhood trauma, low social support, and alexithymia are recognized as risk factors for major depressive disorder (MDD). However, the mechanisms of risk factors, symptoms, and corresponding structural brain abnormalities in MDD are not fully understood. Structural equation modeling (SEM) has advantages in studying multivariate interrelationships. We aim to illustrate their relationships using SEM.

METHODS

313 MDD patients (213 female; mean age 42.49 years) underwent magnetic resonance imaging and completed assessments. We integrated childhood trauma, alexithymia, social support, anhedonia, depression, anxiety, suicidal ideation and cortical thickness into a multivariate SEM.

RESULTS

We first established the risk factors-clinical phenotype SEM with an adequate fit. Cortical thickness results show a negative correlation of childhood trauma with the left middle temporal gyrus (MTG) (p = 0.012), and social support was negatively correlated with the left posterior cingulate cortex (PCC) (p < 0.001). The final good fit SEM (χ2 = 32.92, df = 21, χ2/df = 1.57, CFI = 0.962, GFI = 0.978, RMSEA = 0.043) suggested two pathways, with left PCC thickness mediating the relationship between social support and suicidal ideation, and left MTG thickness mediating between childhood trauma and anhedonia/anxiety.

CONCLUSION

Our findings provide evidence for the impact of risk factor variables on the brain structure and clinical phenotype of MDD patients. Insufficient social support and childhood trauma might lead to corresponding cortical abnormalities in PCC and MTG, affecting the patient's mood and suicidal ideation. Future interventions should aim at these nodes.

The association of brain-derived neurotrophic factor with the diagnosis and treatment response in depression

INTRODUCTION

Recent evidence from the studies evaluating the association between brain derived neurotrophic factor (BDNF) concentration/levels, BDNF Val66Met (rs6265) polymorphism and major depressive disorders, referred as depression, and the association between BDNF levels and/or BDNF Val66Met with the treatment response in depression, is presented.

AREAS COVERED

This mini review focuses on the changes in the peripheral BDNF levels in blood (serum, plasma, platelets) in patients with depression before or after treatment with antidepressant drugs or different therapeutic strategies. In addition, this review describes the recent data on the possible association between different antidepressants/therapeutic strategies and the particular BDNF Val66Met genotypes, evaluating the risk alleles associated with the response in patients with depression.

EXPERT OPINION

BDNF has an important role in the pathophysiology and treatment response in depression. Most data reveal that peripheral BDNF levels are lower before than after antidepressant treatment and might be used as potential biomarkers of therapeutic response. Novel therapeutic strategies should target restoring/increasing BDNF levels.