Abstract
Background
The greater presence of neurodevelopmental antecedants may differentiate schizophrenia from bipolar disorders (BD). Machine learning/pattern recognition allows us to estimate the ...biological age of the brain from structural magnetic resonance imaging scans (MRI). The discrepancy between brain and chronological age could contribute to early detection and differentiation of BD and schizophrenia.
Methods
We estimated brain age in 2 studies focusing on early stages of schizophrenia or BD. In the first study, we recruited 43 participants with first episode of schizophrenia-spectrum disorders (FES) and 43 controls. In the second study, we included 96 offspring of bipolar parents (48 unaffected, 48 affected) and 60 controls. We used relevance vector regression trained on an independent sample of 504 controls to estimate the brain age of study participants from structural MRI. We calculated the brain-age gap estimate (BrainAGE) score by subtracting the chronological age from the brain age.
Results
Participants with FES had higher BrainAGE scores than controls (F(1, 83) = 8.79, corrected P = .008, Cohen’s d = 0.64). Their brain age was on average 2.64 ± 4.15 years greater than their chronological age (matched t(42) = 4.36, P < .001). In contrast, participants at risk or in the early stages of BD showed comparable BrainAGE scores to controls (F(2,149) = 1.04, corrected P = .70, η2 = 0.01) and comparable brain and chronological age.
Conclusions
Early stages of schizophrenia, but not early stages of BD, were associated with advanced BrainAGE scores. Participants with FES showed neurostructural alterations, which made their brains appear 2.64 years older than their chronological age. BrainAGE scores could aid in early differential diagnosis between BD and schizophrenia.
Structural and functional brain alterations are found in adults with depression. It is not known whether these changes are a result of illness or exist prior to disorder onset. Asymptomatic offspring ...of parents with depression offer a unique opportunity to research neural markers of familial risk to depression and clarify the temporal sequence between brain changes and disorder onset. We conducted a systematic review to investigate whether asymptomatic offspring at high familial risk have structural and functional brain changes like those reported in adults with depression. Our literature search resulted in 44 studies on 18,645 offspring ranging from 4 weeks to 25 years old. Reduced cortical thickness and white matter integrity, and altered striatal reward processing were the most consistent findings in high-risk offspring across ages. These alterations are also present in adults with depression, suggesting the existence of neural markers of familial risk for depression. Additional studies reproducing current results, streamlining fMRI data analyses, and investigating underexplored topics (i.e intracortical myelin, gyrification, subcortical shape) may be among the next steps required to improve our understanding of neural markers indexing the vulnerability to depression.
•Neural markers of familial risk for depression have been summarized in infants, children, adolescents, and young adults aged 4 weeks to 25 years.•Across 44 studies, 18,645 asymptomatic youth at familial risk show brain alterations similar to those seen in depression.•Differences in brain structure and function are associated with depression risk and may manifest since early childhood.•Developmental timing and sex-specificity of neural markers require further study.
Background Pharmacogenetic studies aiming to personalize the treatment of depression are based on the assumption that response to antidepressants is a heritable trait, but there is no compelling ...evidence to support this. Methods We estimate the contribution of common genetic variation to antidepressant response with Genome-Wide Complex Trait Analysis in a combined sample of 2799 antidepressant-treated subjects with major depressive disorder and genome-wide genotype data. Results We find that common genetic variants explain 42% (SE = .180, p = .009) of individual differences in antidepressant response. Conclusions These results suggest that response to antidepressants is a complex trait with substantial contribution from a large number of common genetic variants of small effect.
Little is known about the impact of insulin resistance on bipolar disorder.
To examine the relationships between insulin resistance, type 2 diabetes and clinical course and treatment outcomes in ...bipolar disorder.
We measured fasting glucose and insulin in 121 adults with bipolar disorder. We diagnosed type 2 diabetes and determined insulin resistance. The National Institute of Mental Health Life Chart was used to record the course of bipolar disorder and the Alda scale to establish response to prophylactic lithium treatment.
Patients with bipolar disorder and type 2 diabetes or insulin resistance had three times higher odds of a chronic course of bipolar disorder compared with euglycaemic patients (50% and 48.7% respectively v. 27.3%, odds ratio (OR) = 3.07, P = 0.007), three times higher odds of rapid cycling (38.5% and 39.5% respectively v. 18.2%, OR = 3.13, P = 0.012) and were more likely to be refractory to lithium treatment (36.8% and 36.7% respectively v. 3.2%, OR = 8.40, P<0.0001). All associations remained significant after controlling for antipsychotic exposure and body mass index in sensitivity analyses.
Comorbid insulin resistance may be an important factor in resistance to treatment in bipolar disorder.
Abstract The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and ...error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5–10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R2 ) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug.
The sound of a person's voice is commonly used to identify the speaker. The sound of speech is also starting to be used to detect medical conditions, such as depression. It is not known whether the ...manifestations of depression in speech overlap with those used to identify the speaker. In this paper, we test the hypothesis that the representations of personal identity in speech, known as speaker embeddings, improve the detection of depression and estimation of depressive symptoms severity. We further examine whether changes in depression severity interfere with the recognition of speaker's identity. We extract speaker embeddings from models pre-trained on a large sample of speakers from the general population without information on depression diagnosis. We test these speaker embeddings for severity estimation in independent datasets consisting of clinical interviews (DAIC-WOZ), spontaneous speech (VocalMind), and longitudinal data (VocalMind). We also use the severity estimates to predict presence of depression. Speaker embeddings, combined with established acoustic features (OpenSMILE), predicted severity with root mean square error (RMSE) values of 6.01 and 6.28 in DAIC-WOZ and VocalMind datasets, respectively, lower than acoustic features alone or speaker embeddings alone. When used to detect depression, speaker embeddings showed higher balanced accuracy (BAc) and surpassed previous state-of-the-art performance in depression detection from speech, with BAc values of 66% and 64% in DAIC-WOZ and VocalMind datasets, respectively. Results from a subset of participants with repeated speech samples show that the speaker identification is affected by changes in depression severity. These results suggest that depression overlaps with personal identity in the acoustic space. While speaker embeddings improve depression detection and severity estimation, deterioration or improvement in mood may interfere with speaker verification.
Adverse reactions to antidepressants Uher, Rudolf; Farmer, Anne; Henigsberg, Neven ...
British journal of psychiatry
195, Številka:
3
Journal Article
Recenzirano
Odprti dostop
Adverse drug reactions are important determinants of non-adherence to antidepressant treatment, but their assessment is complicated by overlap with depressive symptoms and lack of reliable ...self-report measures.
To evaluate a simple self-report measure and describe adverse reactions to antidepressants in a large sample.
The newly developed self-report Antidepressant Side-Effect Checklist and the psychiatrist-rated UKU Side Effect Rating Scale were repeatedly administered to 811 adult participants with depression in a part-randomised multicentre open-label study comparing escitalopram and nortriptyline.
There was good agreement between self-report and psychiatrists' ratings. Most complaints listed as adverse reactions in people with depression were more common when they were medication-free rather than during their treatment with antidepressants. Dry mouth (74%), constipation (33%) and weight gain (15%) were associated with nortriptyline treatment. Diarrhoea (9%), insomnia (36%) and yawning (16%) were more common during treatment with escitalopram. Problems with urination and drowsiness predicted discontinuation of nortriptyline. Diarrhoea and decreased appetite predicted discontinuation of escitalopram.
Adverse reactions to antidepressants can be reliably assessed by self-report. Attention to specific adverse reactions may improve adherence to antidepressant treatment.
Objective:
Bipolar disorders increase the risk of dementia and show biological and brain alterations, which resemble accelerated aging. Lithium may counter some of these processes and lower the risk ...of dementia. However, until now no study has specifically investigated the effects of Li on brain age.
Methods:
We acquired structural magnetic resonance imaging scans from 84 participants with bipolar disorders (41 with and 43 without Li treatment) and 45 controls. We used a machine learning model trained on an independent sample of 504 controls to estimate the individual brain ages of study participants, and calculated BrainAGE by subtracting chronological from the estimated brain age.
Results:
BrainAGE was significantly greater in non-Li relative to Li or control participants, F(2, 125) = 10.22, p < 0.001, with no differences between the Li treated and control groups. The estimated brain age was significantly higher than the chronological age in the non-Li (4.28 ± 6.33 years, matched t(42) = 4.43, p < 0.001), but not the Li-treated group (0.48 ± 7.60 years, not significant). Even Li-treated participants with partial prophylactic treatment response showed lower BrainAGE than the non-Li group, F(1, 64) = 4.80, p = 0.03.
Conclusions:
Bipolar disorders were associated with greater, whereas Li treatment with lower discrepancy between brain and chronological age. These findings support the neuroprotective effects of Li, which were sufficiently pronounced to affect a complex, multivariate measure of brain structure. The association between Li treatment and BrainAGE was independent of long-term thymoprophylactic response and thus may generalize beyond bipolar disorders, to neurodegenerative disorders.
Primary prevention in individuals at Clinical High Risk for psychosis (CHR-P) can ameliorate the course of psychotic disorders. Further advancements of knowledge have been slowed by the standstill of ...the field, which is mostly attributed to its epidemiological weakness. The latter, in turn, underlies the limited identification power of at-risk individuals and the relatively modest ability of CHR-P interviews to rule-in a state of risk for psychosis. In the first part, this perspective review discusses these limitations and traces a new approach to overcome them. Theoretical concepts to support a Psychosis Polyrisk Score (PPS) integrating genetic and non-genetic risk and protective factors for psychosis are presented. The PPS hinges on recent findings indicating that risk enrichment in CHR-P samples is accounted for by the accumulation of non-genetic factors such as: parental and sociodemographic risk factors, perinatal risk factors, later risk factors, and antecedents. In the second part of this perspective review we present a prototype of a PPS encompassing core predictors beyond genetics. The PPS prototype may be piloted in the next generation of CHR-P research and combined with genetic information to refine the detection of individuals at-risk of psychosis and the prediction of their outcomes, and ultimately advance clinical research in this field.
Indirect evidence suggests that common genetic variation contributes to individual differences in antidepressant efficacy among individuals with major depressive disorder, but previous studies may ...have been underpowered to detect these effects.
A meta-analysis was performed on data from three genome-wide pharmacogenetic studies (the Genome-Based Therapeutic Drugs for Depression GENDEP project, the Munich Antidepressant Response Signature MARS project, and the Sequenced Treatment Alternatives to Relieve Depression STAR*D study), which included 2,256 individuals of Northern European descent with major depressive disorder, and antidepressant treatment outcomes were prospectively collected. After imputation, 1.2 million single-nucleotide polymorphisms were tested, capturing common variation for association with symptomatic improvement and remission after up to 12 weeks of antidepressant treatment.
No individual association met a genome-wide threshold for statistical significance in the primary analyses. A polygenic score derived from a meta-analysis of GENDEP and MARS participants accounted for up to approximately 1.2% of the variance in outcomes in STAR*D, suggesting a weakly concordant signal distributed over many polymorphisms. An analysis restricted to 1,354 individuals treated with citalopram (STAR*D) or escitalopram (GENDEP) identified an intergenic region on chromosome 5 associated with early improvement after 2 weeks of treatment.
Despite increased statistical power accorded by meta-analysis, the authors identified no reliable predictors of antidepressant treatment outcome, although they did identify modest, direct evidence that common genetic variation contributes to individual differences in antidepressant response.
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