Materiały źródłowe

• #1 Prognosis and improved outcomes in major depression: a review

  https://pmc.ncbi.nlm.nih.gov/articles/PMC6447556/

  Treatment outcomes for major depressive disorder (MDD) need to be improved. Presently, no clinically relevant tools have been established for stratifying subgroups or predicting outcomes. […] The results show that early recognition and treatment are crucial, as duration of untreated depression correlates with worse outcomes. Early improvement is associated with response and remission, while comorbidities prolong course of illness. […] Clear evidence of an inverse relationship between duration of episode and treatment outcome (either response or remission) underscores the importance of early intervention in MDD. […] In particular, replicable prospective and retrospective studies indicate that shorter duration of untreated disease both in terms of first and recurrent episodes is a prognostic factor indicating better treatment response and better long-term outcomes.

• #2 Precision medicine for mood disorders: objective assessment, risk prediction, pharmacogenomics, and repurposed drugs | Molecular Psychiatry

  https://www.nature.com/articles/s41380-021-01061-w

  Mood disorders (depression, bipolar disorders) are prevalent and disabling. They are also highly co-morbid with other psychiatric disorders. Currently there are no objective measures, such as blood tests, used in clinical practice, and available treatments do not work in everybody. […] The development of blood tests, as well as matching of patients with existing and new treatments, in a precise, personalized and preventive fashion, would make a significant difference at an individual and societal level. […] Recent work by us has identified blood gene expression biomarkers that track suicidality, a tragic behavioral outcome of mood disorders, using powerful longitudinal within-subject designs, validated them in suicide completers, and tested them in independent cohorts for ability to assess state (suicidal ideation), and ability to predict trait (future hospitalizations for suicidality).

• #3 Seasonality in mood disorders: Probing association of accelerometer-derived physical activity with daylength and solar insolation | PLOS Mental Health

  https://journals.plos.org/mentalhealth/article?id=10.1371/journal.pmen.0000124

  Mood disorders are the leading cause of disability worldwide. Up to 30 percent of individuals with major depressive disorder (MDD) and bipolar disorder (BD) display a seasonal pattern of onset, a phenomenon now recognized in the official diagnostic manuals (DSM-5 and ICD-11). […] Our study presents a generalizable strategy to decipher the complex interplay between sunlight, physical activity, and depressed state using open-source digital tools. The ability to identify mood disturbances, particularly in seasonally susceptible individuals, using passive digital biomarker data offers great promise in informing next-generation predictive, personalized diagnostics in mental health. […] A significant fraction (10-30%) of affected individuals exhibit changes in various psychosocial areas of functioning (motivation, sleep, and mood) that are dictated by seasonal patterns of recurrent (hypo)manic and depressive episodes.

• #4 Prognosis and improved outcomes in major depression: a review | Translational Psychiatry

  https://www.nature.com/articles/s41398-019-0460-3

  Treatment outcomes for major depressive disorder (MDD) need to be improved. Presently, no clinically relevant tools have been established for stratifying subgroups or predicting outcomes. […] The results show that early recognition and treatment are crucial, as duration of untreated depression correlates with worse outcomes. Early improvement is associated with response and remission, while comorbidities prolong course of illness. […] Clear evidence of an inverse relationship between duration of episode and treatment outcome (either response or remission) underscores the importance of early intervention in MDD. […] In particular, replicable prospective and retrospective studies indicate that shorter duration of untreated disease both in terms of first and recurrent episodes is a prognostic factor indicating better treatment response and better long-term outcomes.

• #5 Predicting vulnerability to mood disorders and treatment response | Progress In Mind

  https://mea-qa9.progress.im/en/content/predicting-vulnerability-mood-disorders-and-treatment-response

  Major depressive disorder (MDD) is ultimately about how the brain responds to the environment, but many variables contribute to the clinical phenotype and response to treatment, experts explained to a large audience at CINP 2018. […] Combining biomarkers enables MDD stratification and prediction of antidepressant response; polygenic score predicts hippocampal volume and correlates with treatment outcome; and big data analysis of variables enables prediction of individual mood disorder vulnerability and treatment response. […] Early treatment for MDD to pre-empt the clinical manifestations can prevent changes in the brain. […] Each mood disorder episode fundamentally changes the brain, he added, so the aim is to institute treatment early to pre-empt the clinical manifestations; and in 21st century medicine there are three points of intervention: very early preventive measures to alter genetic disease susceptibility, interventions at tipping points identified by molecular markers and imaging changes to prevent the development of clinical manifestations, treatments to address the clinical manifestations.

• #6 Mental Health Prediction Model in Eswatini Using Machine Learning Techniques | IntechOpen

  https://www.intechopen.com/online-first/1216202

  Mental health is a very important aspect of our daily life because it includes our emotional, psychological, and social well-being. […] This study concludes that early detection, such as identifying individuals at risk of developing anxiety or depression, can enable early intervention and prevent the progression of symptoms. […] The timely identification of patients who are at risk of a mental health crisis can lead to improved outcomes and mitigate burdens and costs. […] The high prevalence of mental illness combined with the need for effective health care has led to an increase in the investigation of how machine learning can assist in the detection and diagnosis of mental health problems. […] This project has confirmed from the existing literature that indeed ML models can achieve high accuracy in predicting anxiety and depression. […] Overall, while challenges remain, developing predictive ML models for anxiety and depression holds significant promise for improving mental health care.

• #7 Prognosis and improved outcomes in major depression: a review

  https://pmc.ncbi.nlm.nih.gov/articles/PMC6447556/

  Another important clinical variable is time to antidepressant response. […] Early response to antidepressant treatment appears to occur independently of treatment modality or outcome parameters. […] Early improvement in work productivity was a significant positive predictor of higher remission rates after three and seven months of treatment. […] The influence of sociodemographic factors such as age, age of onset, gender, and number of previous episodes on treatment outcome has been investigated with mixed results. […] High stress levels significantly influence outcomes in MDD patients who are prone to vulnerable states, such as those with high levels of neuroticism. […] Psychiatric comorbidity has been shown to influence outcome in both treated and untreated patients. […] Studies have found that elevated baseline anxiety symptoms or comorbid anxiety disorder are associated with worse antidepressant response to first-line selective serotonin reuptake inhibitors (SSRIs) or second-line treatment strategies.

• #8 Prognosis and improved outcomes in major depression: a review | Translational Psychiatry

  https://www.nature.com/articles/s41398-019-0460-3

  Another important clinical variable is time to antidepressant response. […] Early response to antidepressant treatment appears to occur independently of treatment modality or outcome parameters. […] Lower baseline function and quality of life including longer duration of the current index episode have been associated with lower remission rates to various types of antidepressant treatments. […] The influence of sociodemographic factors such as age, age of onset, gender, and number of previous episodes on treatment outcome has been investigated with mixed results. […] High stress levels significantly influence outcomes in MDD patients who are prone to vulnerable states, such as those with high levels of neuroticism. […] Psychiatric comorbidity has been shown to influence outcome in both treated and untreated patients.

• #9 Prognosis and improved outcomes in major depression: a review | Translational Psychiatry

  https://www.nature.com/articles/s41398-019-0460-3

  Studies have found that elevated baseline anxiety symptoms or comorbid anxiety disorder are associated with worse antidepressant response to first-line selective serotonin reuptake inhibitors (SSRIs) or second-line treatment strategies. […] Data from the Sequential Treatment Alternatives to Relieve Depression (STAR*D) study indicate that roughly one-third (34.8%) of all MDD patients are free of any comorbidity; the most frequent comorbid Axis-I disorders are social phobia, generalized anxiety disorder, PTSD, and obsessive-compulsive disorder. […] MDD and several physical diseases including cardiovascular disease and diabetes appear to have bidirectional effects on disease trajectory, yet pathophysiologic links are most likely complex and have to be elucidated. […] Structural markers of antidepressant treatment outcomes suggest that hippocampal volumes are related to response and remission.

• #10 Prognosis and improved outcomes in major depression: a review

  https://pmc.ncbi.nlm.nih.gov/articles/PMC6447556/

  MDD and several physical diseases including cardiovascular disease and diabetes appear to have bidirectional effects on disease trajectory. […] Structural markers of antidepressant treatment outcomes suggest that hippocampal volumes are related to response and remission. […] Consistent with neuroinflammatory processes, elevated levels of C-reactive protein (CRP), tumor necrosis factor alpha (TNF), and interleukin-6 (IL-6) have been reported in a subset of MDD patients. […] A longitudinal study found that lower CRP levels were associated with quicker response to SSRIs, an association not observed for SSRI-bupropion combination therapy. […] The research reviewed above indicates that early recognition and early adequate treatment at illness onset are preferable to watch-and-wait strategies.

• #11 Prognosis and improved outcomes in major depression: a review | Translational Psychiatry

  https://www.nature.com/articles/s41398-019-0460-3

  A meta-analysis confirmed that low baseline hippocampal volumes are associated with negative outcomes. […] Elevated levels of C-reactive protein (CRP), tumor necrosis factor alpha (TNF), and interleukin-6 (IL-6) have been reported in a subset of MDD patients. […] A longitudinal study found that lower CRP levels were associated with quicker response to SSRIs. […] The authors suggested that alternative splicing could lead to shifts in the proportion of isoforms and altered biological functions of these proteins. […] While some studies have suggested that depressive subtypes in MDD including anxious, mixed, melancholic, atypical, and psychotic depression respond differently to antidepressant treatment, this literature is mixed. […] Cross-sectional data from the GSRD identified a number of risk factors linked to TRD, including comorbidity, suicide risk, episode severity, number of hospitalizations, episode recurrence, early-onset, melancholic features, and non-response at first treatment. […] This review outlines important clinical, psychosocial, and biological factors associated with response and remission to antidepressant treatment.

• #12 Predicting vulnerability to mood disorders and treatment response | Progress In Mind

  https://mea-qa9.progress.im/en/content/predicting-vulnerability-mood-disorders-and-treatment-response

  Professor Nemeroff added that when all variables are subjected to sophisticated data modeling, it is possible to obtain predictions of individual mood disorder vulnerability and treatment response. […] The PReDICT (Predictors of remission in depression to individual and combined treatments) study’s evaluation of clinical, biological, genetic, and personality factors on treatment outcomes demonstrated that combining biomarkers enables MDD stratification and prediction of antidepressant response, explained Professor Binder. […] Variables and biomarkers that predict response to treatment and outcome for patients with MDD include: environmental risk factors such as trauma, abuse and neglect, especially in childhood; genetic factors, which contribute 30% of the polygenic risk for MDD; clinical variables such as comorbid anxiety and post-traumatic stress disorder; structural changes in the brain as demonstrated by the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium. […] Hippocampal volume correlates with treatment outcome. A small hippocampus is associated with long-term depression. […] Polygenic score predicts hippocampal volume and correlates with treatment outcome overall, said Professor Binder. A small hippocampus is associated with long-term depression.

• #13 Precision medicine for mood disorders: objective assessment, risk prediction, pharmacogenomics, and repurposed drugs

  https://scholarworks.indianapolis.iu.edu/items/493d6083-3664-4088-9941-d16b8a887667

  Mood disorders (depression, bipolar disorders) are prevalent and disabling. […] Currently there are no objective measures, such as blood tests, used in clinical practice, and available treatments do not work in everybody. […] Recent work by us has identified blood gene expression biomarkers that track suicidality, a tragic behavioral outcome of mood disorders, using powerful longitudinal within-subject designs, validated them in suicide completers, and tested them in independent cohorts for ability to assess state (suicidal ideation), and ability to predict trait (future hospitalizations for suicidality). […] We further analyzed the biological pathways and networks for the top candidate biomarkers, showing that circadian, neurotrophic, and cell differentiation functions are involved, along with serotonergic and glutamatergic signaling, supporting a view of mood as reflecting energy, activity and growth.

• #14 Precision medicine for mood disorders: objective assessment, risk prediction, pharmacogenomics, and repurposed drugs | Molecular Psychiatry

  https://www.nature.com/articles/s41380-021-01061-w

  We further analyzed the biological pathways and networks for the top candidate biomarkers, showing that circadian, neurotrophic, and cell differentiation functions are involved, along with serotonergic and glutamatergic signaling, supporting a view of mood as reflecting energy, activity and growth. […] We tested in independent cohorts of psychiatric patients the ability of each of these 26 top candidate biomarkers to assess state (mood (SMS-7), depression (HAMD), mania (YMRS)), and to predict clinical course (future hospitalizations for depression, future hospitalizations for mania). […] We also used the biomarker signatures to bioinformatically identify new/repurposed candidate drugs. […] Overall, our studies provide objective assessments, targeted therapeutics, and monitoring of response to treatment, that enable precision medicine for mood disorders.

• #15 Outcome Prediction for Patients with Bipolar Disorder Using Prodromal and Onset Data

  https://www.mdpi.com/2076-3417/13/3/1552

  Outcome prediction can facilitate physician and patient decision making as well as disease management. […] The key research question in this study was how well (in terms of AUC and accuracy) the adverse outcome (all-cause mortality and number of all-cause hospitalizations) of bipolar disorder (BD) can be predicted after the initial diagnosis. […] The AUC of the predictive models in various patient populations is often in the lower 70% range, only occasionally reaching 80%. […] The ability to predict a patient outcome at the time of the initial BD diagnosis has several implications. Patients who are predicted to have poor outcomes can be closely monitored and considered for additional interventions by clinicians. […] This study represents a first step in applying deep learning and temporal images to mental health risk prediction with promising results. […] Applying deep learning and temporal graphics is a promising approach to predicting adverse health outcomes of BD patients. Depending on the number and type of hospitalization as outcomes, the best accuracies and AUCs reached over 90%.

• #16 Predicting the risk and timing of major mood disorder in offspring of bipolar parents: exploring the utility of a neural network approach | International Journal of Bipolar Disorders | Full Text

  https://journalbipolardisorders.springeropen.com/articles/10.1186/s40345-021-00228-2

  Bipolar disorder onset peaks over early adulthood and confirmed family history is a robust risk factor. […] Individualized risk prediction would be helpful for identifying those young people most at risk and to inform targeted intervention. […] Overall, for predictive performance, PLANN outperformed the more traditional discrete survival model for 3-year and 5-year predictions. […] PLANN was better able to discriminate or rank individuals based on their risk of developing a major mood disorder, better able to predict the probability of developing a major mood disorder and better able to identify individuals who would be diagnosed in future time intervals. […] This evaluation of PLANN is a useful step in the investigation of using neural networks as tools in the prediction of mood disorders in at-risk individuals and the potential that neural networks have in this field.

• #17 Predicting the risk and timing of major mood disorder in offspring of bipolar parents: exploring the utility of a neural network approach | International Journal of Bipolar Disorders | Full Text

  https://journalbipolardisorders.springeropen.com/articles/10.1186/s40345-021-00228-2

  The prediction of which offspring are at greater risk of major mood disorder over time is important for clinical researchers, as it may allow for more proactive monitoring and prevention. […] PLANN outperformed the discrete survival model on the Brier score which measures the difference between observed and predicted values. […] PLANN was better able to discriminate or rank individuals based on their risk of developing major mood disorder (i.e., higher time-dependent c-indices) and better able to predict the probability of developing major mood disorder (i.e., lower Brier scores). […] Overall, for predictive performance, PLANN outperformed the logistic model for 3-year and 5-year predictions. […] The results were mixed for 1-year predictions, with the discrete survival model outperforming PLANN for AUC.

• #18 Logo for Cambridge Core from Cambridge University Press. Click to return to homepage.

  https://www.cambridge.org/core/journals/the-british-journal-of-psychiatry/article/prediction-of-longerterm-outcome-of-treatmentresistant-depression-in-tertiary-care/79683F91C6D3398D0877FC16828070B8

  Systematic studies on the outcome of treatment-resistant depression are scarce. […] To describe the longer-term outcome and predictors of outcome in treatment-resistant depression. […] The majority of participants attained full remission (60.2%), most of whom (48.3% of total sample) showed sustained recovery (full remission for at least 6 months). […] A substantial minority had persistent subsyndromal depression (19.5%) or persistent depressive episode (20.3%). […] Diagnosis of bipolar treatment-resistant depression and poorer social support were associated with early relapse, whereas strong social support, higher educational status and milder level of treatment resistance measured with the Maudsley Staging Method were associated with achieving quicker remission. […] Although many patients with treatment-resistant depression experience persistent symptomatology even after intensive, specialist treatment, most can achieve remission.

• #19 Logo for Cambridge Core from Cambridge University Press. Click to return to homepage.

  https://www.cambridge.org/core/journals/the-british-journal-of-psychiatry/article/prediction-of-longerterm-outcome-of-treatmentresistant-depression-in-tertiary-care/79683F91C6D3398D0877FC16828070B8

  Overall, 48.3% (n = 57) of the cohort met recovery criteria, and 11.9% (n = 14) had met remission criteria without ever meeting recovery criteria. […] In terms of unfavourable outcome, 39.8% had persistent symptomatology throughout the follow-up period, either because of persistent subthreshold/subsyndromal symptoms (19.5%) or persistent depressive episode (20.3%). […] Mortality is one of the indicators of unfavourable outcome in depression. […] Three main factors were associated with achieving remission during follow-up: severity of treatment-resistant depression measured with the MSM, educational achievement and level of social support. […] The two factors independently associated with relapse were poor social support and receiving a diagnosis of bipolar depression. […] Use of MAOIs while an in-patient was independently associated with remission at point of discharge controlling for other treatments, particularly for unipolar treatment-resistant depression.

• #20 Logo for Cambridge Core from Cambridge University Press. Click to return to homepage.

  https://www.cambridge.org/core/journals/the-british-journal-of-psychiatry/article/prediction-of-longerterm-outcome-of-treatmentresistant-depression-in-tertiary-care/79683F91C6D3398D0877FC16828070B8

  The choice of treatment and presence of good social support may affect remission rates, whereas those with low social support and a bipolar diathesis should be considered at higher risk of early relapse. […] We suggest that future work to improve the long-term outcome in this disabling form of depression might focus on social interventions to improve support, and the role of neglected pharmacological interventions such as MAOIs. […] The mean severity of the level of resistance measured using the MSM was 10.1 (s.d. = 2.2), suggesting that most participants had moderately severe or severe treatment-resistant depression. […] The MSM was developed based on theoretical considerations, and the staging method has shown good predictive validity for both short-term and longer-term outcomes. […] The main outcomes of interest were remission, recovery, relapse and persistence of depressive episode.

• #21 The big five model in bipolar disorder: a latent profile analysis and its impact on longterm illness severity | International Journal of Bipolar Disorders | Full Text

  https://journalbipolardisorders.springeropen.com/articles/10.1186/s40345-021-00248-y

  Using a personality typing approach, we investigated the relationship between personality profiles and the prediction of longterm illness severity in patients with bipolar disorder (BD). […] The current study firstly aimed to identify latent classes of NEO-FFI profiles, and, secondly, to examine their impact on the longterm prognosis of BD. […] In the regression analysis, higher vulnerability predicted a higher longterm Morbidity Index (R2=0.28). […] This knowledge is valuable in a variety of clinical contexts including early detection, intervention planning and treatment process. […] The aims of this study are therefore twofold: First, we aim to classify BD based on patients’ NEO-FFI scores by applying latent profile analysis and to characterize the emerging latent classes. In a second step, we aim to predict the treatment course (indicated by the MI) using the obtained personality classes.

• #22 The big five model in bipolar disorder: a latent profile analysis and its impact on longterm illness severity | International Journal of Bipolar Disorders | Full Text

  https://journalbipolardisorders.springeropen.com/articles/10.1186/s40345-021-00248-y

  We successfully identified subgroups of BD patients based on their NEO-FFI profiles. […] In addition, we observed that a poorer longitudinal outcome based on the MI was associated with belonging to either the vulnerable or highly vulnerable class. Personality classes accounted for 27% (adjusted R2=0.13) of the variation of the MI. […] The predictive value of the personality variables may be explained by several factors. […] The MI considers both the duration of an affective illness episode, as well as the severity of depressive and/or (hypo-)manic symptoms. […] The use of the MI might serve as a core asset in predicting clinical courses of BD by personality aspects where past studies using single measures failed to do so.

• #23 Seasonality in mood disorders: Probing association of accelerometer-derived physical activity with daylength and solar insolation | PLOS Mental Health

  https://journals.plos.org/mentalhealth/article?id=10.1371/journal.pmen.0000124

  Despite general consensus that symptoms of MDD and BD display a seasonal pattern of fluctuations in many individuals, several critical unresolved issues remain. […] Significant challenges remain in identifying robust and reproducible biomarkers that are needed to improve BD and MDD patient outcomes. […] The use of wrist-worn actigraphs constitutes a scalable, non-invasive, time-sensitive, and cost-effective approach for detecting MDD or BD characterized by notable changes in goal-directed behavior, energy level, movement, and disruption of the sleep-wake cycle. […] Our rationale for including two sunlight parameters (day length or photoperiod and sunlight intensity or solar insolation) in our exploratory models is based on a series of recent studies demonstrating positive association between day length and physical activity.

• #24 Seasonality in mood disorders: Probing association of accelerometer-derived physical activity with daylength and solar insolation | PLOS Mental Health

  https://journals.plos.org/mentalhealth/article?id=10.1371/journal.pmen.0000124

  In agreement with prior studies, we found that depressed state was associated with lower accelerometer-derived daytime activity. This was accompanied by positive associations between photoperiod and daytime activity as well as solar insolation and daytime activity, both comparable in magnitude. […] Overall, the ability to distinguish between depressed and healthy state using passive sensor data offers promise in informing next-generation predictive depression diagnostics.

• #25 Precision medicine for mood disorders: objective assessment, risk prediction, pharmacogenomics, and repurposed drugs | Molecular Psychiatry

  https://www.nature.com/articles/s41380-021-01061-w

  Blood biomarkers are emerging as important tools in disorders where subjective self-report of an individual or clinical impression of a healthcare professional are not always reliable, and for predicting future risk before the disorder (re-)occurs. […] We endeavored to use a similar comprehensive approach to identify more definitive biomarkers for mood disorders in general, and depression in particular. […] We also analyzed the biological pathways and networks they are involved in. […] We tested the biomarkers in all subjects in the test cohort, as well as in a more personalized fashion by gender and psychiatric diagnosis. […] We also conducted similar analyses for predicting high mood, clinical mania state, and future hospitalizations with mania. […] We also used the biomarkers to match to existing psychiatric medications, as well as to identify and potentially repurpose new drugs for mood disorders treatment using bioinformatics analyses. […] These biomarkers should be tested individually as well as tested as polygenic panels of biomarkers in future clinical studies and practical clinical applications in the field.

• #26 Precision medicine for mood disorders: objective assessment, risk prediction, pharmacogenomics, and repurposed drugs

  https://scholarworks.indianapolis.iu.edu/items/493d6083-3664-4088-9941-d16b8a887667

  Fourth, we tested in independent cohorts of psychiatric patients the ability of each of these 26 top candidate biomarkers to assess state (mood (SMS-7), depression (HAMD), mania (YMRS)), and to predict clinical course (future hospitalizations for depression, future hospitalizations for mania). […] Again, using SLC6A4 as the cutoff, twelve top biomarkers had the strongest overall evidence for tracking and predicting depression after all four steps: NRG1, DOCK10, GLS, PRPS1, TMEM161B, GLO1, FANCF, HNRNPDL, CD47, OLFM1, SMAD7, and SLC6A4. […] These panels of biomarkers have practical implications for distinguishing between depression and bipolar disorder. […] Overall, our studies provide objective assessments, targeted therapeutics, and monitoring of response to treatment, that enable precision medicine for mood disorders.

• #27 Prognosis and improved outcomes in major depression: a review

  https://pmc.ncbi.nlm.nih.gov/articles/PMC6447556/

  Together, these factors contribute toward treatment resistance, which has gained a substantial amount of importance as a patient-stratifying variable. […] This review outlines important clinical, psychosocial, and biological factors associated with response and remission to antidepressant treatment. […] Key discoveries into novel rapid-acting substances, in concert with improvements in brain stimulation techniques, may also result in significantly improved treatment outcomes in formerly hard-to-treat patients.

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