Biomarkers in Neuropsychiatry最新文献

Background

Many published studies report that antipsychotic therapy is associated with thrombotic tendency. However, fewer studies have examined whether thrombotic tendency exists in unmedicated schizophrenia (SZ) or other psychoses. In addition, some studies have reported a full remission of psychotic symptoms with warfarin, a well-known anticoagulant, raising the possibility that psychosis may be associated with thrombotic tendency. Here, we summarize the available literature on biomarkers of thrombotic tendency in unmedicated patients with SZ and other psychoses.

Methods

A PubMed search using the keywords “psychosis” OR “schizophrenia” AND (“coagulation” OR “tissue plasminogen activator” OR “thromboembolism”) for studies published between 2012 and 2023 yielded 290 results. Inclusion criteria were 1) controlled studies, 2) studies including patients with psychosis, 3) English language. Exclusion criteria included 1) review articles, 2) case reports, 3) focus on antipsychotics as a factor in thrombotic tendency.

Results

Seven studies met criteria and were included for qualitative synthesis in this review. Five studies included patients with SZ and related psychoses, while two studies also included patients with major depression and bipolar disorders. Numerous plasma proteins involved in regulating coagulation were identified as being low in patients with SZ, including fibrinolytic enzymes such as tissue-type plasminogen activator (tPA), plasmin, protein S, and plasminogen, although one study found that tPA was reduced in chronic SZ but elevated in first-episode SZ (FES) patients.

Discussion

Those reports suggestive of thrombotic tendency in schizophrenia and related psychoses warrant further investigation, especially in drug-naïve first episode samples as compared to chronic patients, where antipsychotic treatment may contribute to thrombotic tendency. If confirmed, therapeutic strategies with anticoagulants like tPA may represent a novel approach to managing schizophrenia.

The past decade witnessed substantial discoveries related to the psychosis spectrum. Many of these discoveries resulted from pursuits of objective and quantifiable biomarkers in tandem with the application of analytical tools such as machine learning. These approaches provided exciting new insights that significantly helped improve precision in diagnosis, prognosis, and treatment. This article provides an overview of how machine learning has been employed in recent biomarker discovery research in the psychosis spectrum, which includes schizophrenia, schizoaffective disorders, bipolar disorder with psychosis, first episode psychosis, and clinical high risk for psychosis. It highlights both human and animal model studies and explores a varying range of the most impactful biomarkers including cognition, neuroimaging, electrophysiology, and digital markers. We specifically highlight new applications and opportunities for machine learning to impact noninvasive symptom monitoring, prediction of future diagnosis and treatment outcomes, integration of new methods with traditional clinical research and practice, and personalized medicine approaches.

We investigated affective processing in aphantasia (= absent or reduced vividness of mental imagery), considering a possible overlap with alexithymia (= deficits in identifying and describing emotions), as reduced vividness of mental imagery is also reported in alexithymia. Study 1 assessed physiological reactions and self-reported sympathy in n = 30 individuals with aphantasia and n = 75 controls when confronted to visual and verbal material showing people in distress. Results demonstrated that individuals with aphantasia show reduced emotional responses, especially to verbal stimuli. This is of particular importance given the higher prevalence of alexithymic symptoms in aphantasic participants, notably in externally-oriented thinking and difficulties in describing feelings. An additional mediation analysis confirmed that vividness of visual imagery mediated the association between alexithymia and self-reported sympathy. Study 2 extended our exploration to the recognition of emotions in others using the same sample. Despite accurate recognition of emotions, individuals with aphantasia exhibited significantly slower response times, suggesting less efficient strategies that do not involve mental imagery. Our findings highlight the crucial role of mental imagery in the interplay of cognitive functions and affective processes, demonstrating how conditions such as aphantasia and alexithymia can affect sympathy and, more generally, emotions.

Background

Smartphone-based digital phenotyping can provide novel transdiagnostic markers of mental illness including circadian routines and anhedonia. In proposing transdiagnostic digital phenotypes for circadian routines and anhedonia in depression and bipolar disorder patients, this paper explores their derivation, comparison to naive models, and replicability across two different research sites/teams.

Methods

84 participants (bipolar disorder, depression, controls) used the mindLAMP app for 12 weeks to capture digital phenotypes on their personal smartphones. mindLAMP was used to deliver surveys about mood symptoms while collecting device acceleration, geolocation, and screen on/off state. Participant chronotype was derived from this sensor data. Within-participant and between-participant models were created to assess how time-varying features collected through digital phenotyping could predict weekly anhedonia survey responses.

Results

Within-person models outperformed between-person models in predicting anhedonia. Chronotype was the strongest predictor of weekly anhedonia scores as indicated by Shapley scores. Shapley scores also revealed that many of the time-varying predictor variables are significant but differ in their direction of action.

Discussion

This analysis reveals the meaningful but potentially misleading nature of digital phenotyping signals. Results suggest that each participant has a unique set of relationships between time-varying digital phenotype variables; therefore, it is challenging to predict trends between participants. Bayesian models, with appropriate population priors, may offer the next step for improving the potential of personalized digital phenotyping insights.

Treatment-resistant depression, defined as a failure of at least two oral antidepressants of adequate dose and duration in the current episode, is a debilitating condition with low rates of response and remission. Intranasal esketamine is the first medication approved by regulatory authorities for difficult to treat depressive episodes that builds directly on the discovery of the rapid antidepressant effects of intravenously administered racemic ketamine. Approved in the United States in 2019, and subsequently, in many countries worldwide, intranasal esketamine is indicated in conjunction with any new or previously taken oral antidepressant medication of the clinician’s choosing for treatment-resistant depression in adults. It is also indicated for the treatment of major depressive disorder in adults with acute suicidal ideation or behavior who may or may not be treatment-naive or have treatment-resistant depression. This article outlines the case for the efficacy, safety profile, and feasibility of acute, short-term intranasal esketamine followed by the long-term continuation of intranasal esketamine versus long-term off-label intravenous ketamine in treatment-resistant depression. The article further suggests biomarkers to predict response that warrant further study.

Prenatal emotional distress is common in pregnant women. Altered emotional distress can occur from the very beginning to the end of pregnancy. Heart rate variability (HRV) has recently become considered to be a potentially reliable psychophysiological stress biomarker in adults. In the current study, we evaluated ultra-short-term HRV (1-minute measurement) as a psychophysiological biomarker by examining the association between HRV parameters and self-reported prenatal emotional distress among pregnant women (N = 230) across three trimesters of pregnancy.

Results

Prenatal emotional distress was associated with a lower root mean square of successive differences between normal heartbeats (RMSSD), NN50, and SDNN Index among pregnant women who are in the second trimester. For women in the first and third trimester of pregnancy, prenatal emotional distress was not significantly correlated with any HRV indicators.

Limitations

The cross-sectional nature of our results limits the directional expression and assessment of the relationships, and longitudinal studies that target the recruitment of more pregnant women with subtypes of emotional distress issues are also needed.

Conclusions

Time-domain parameters of low HRV (associated with reduced parasympathetic activity) can potentially serve as an efficient psychophysiological biomarker for prenatal emotional distress in the second trimester of pregnancy. However, the time-domain HRV indicators in pregnant women in the first and third trimesters may be affected by other physiological and psychological fluctuations, thus decreasing the HRV biomarker’s efficiency in predicting their prenatal emotional distress.

As many as one third of the patients diagnosed with schizophrenia do not respond to first-line antipsychotic medication. This group may benefit from the atypical antipsychotic medication clozapine, but initiation of treatment is often delayed, which may worsen prognosis. Predicting which patients do not respond to traditional antipsychotic medication at the onset of symptoms would provide fast-tracked treatment for this group of patients. We collected data from patient records of 38 first-episode psychosis patients, of whom seven did not respond to traditional antipsychotic medications. We used clinical data including medical records, voxel-based morphometry MRI data and inter-subject correlation fMRI data, obtained during movie viewing, to predict future treatment resistance. Using a neural network model, we correctly predicted future treatment resistance in six of the seven treatment resistance patients and 25 of 31 patients who did not require clozapine treatment. Prediction improved significantly when using imaging data in tandem with clinical data. The accuracy of the neural network model was significantly higher than the accuracy of a support vector machine algorithm. These results support the notion that treatment resistant schizophrenia could represent a separate entity of psychotic disorders, characterized by morphological and functional changes in the brain which could represent biomarkers detectable at early onset of symptoms.

Background

Stress and trauma significantly contribute to various psychiatric disorders, including schizophrenia. Dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis, particularly abnormal cortisol secretion, is implicated in schizophrenia's pathophysiology. Salivary cortisol, a convenient measure, provides insights into HPA axis activity. This systematic review and meta-analysis assess salivary cortisol levels in individuals with schizophrenia compared to healthy controls, evaluating its potential as a biomarker for schizophrenia.

Aim

To assess the differences in salivary cortisol levels at baseline and in response to stress between patients with schizophrenia and healthy controls, determining the reliability of salivary cortisol as a biomarker for schizophrenia.

Methods

A systematic review and meta-analysis were conducted following PRISMA guidelines. We searched MEDLINE/PubMed, Cochrane Central, and EMBASE for human studies on schizophrenia and controls published from January 1, 2013, to July 1, 2023, reporting baseline or post-stress salivary cortisol levels. Exclusions were non-human studies, non-specific schizophrenia diagnoses, or missing cortisol data. Two reviewers independently extracted data and appraised quality, resolving discrepancies by consensus. Due to significant heterogeneity (I² = 94 % for baseline, 75 % for stress response), a random effects model was used.

Results

Nineteen studies were initially selected, with twelve excluded due to non-specific schizophrenia diagnoses and missing cortisol data. The final meta-analysis included seven studies with 507 schizophrenia patients and 175 healthy controls. Key findings include:

Baseline cortisol levels

No significant difference in baseline salivary cortisol levels between schizophrenia patients and healthy controls (pooled estimate: −0.02, 95 % CI: −0.47–0.42).

Cortisol response to stress

No significant difference in post-stress cortisol levels between the two groups (pooled estimate: 0.03, 95 % CI: −1.84–1.79).

Study variability

High variability across studies due to differences in design, patient characteristics, and cortisol measurement techniques. Despite some evidence of altered cortisol dynamics in schizophrenia, high variability in methodologies and confounding factors like medication use and psychosocial stressors complicate definitive conclusions.

Conclusion

The review underscores the potential of cortisol as a biomarker for stress response; however, its use in diagnosing schizophrenia remains inconclusive. Individual variability and methodological differences limit the diagnostic accuracy of salivary cortisol. Future research with larger sample sizes, uniform methodologies, and comprehensive control for confounding variables is essential to elucidate the role of salivary corti