Abigail Ortiz

Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 Clinical Guidelines for the Management of Adults with Major Depressive Disorder Section 5. Complementary and Alternative Medicine Treatments

Background: The Canadian Network for Mood and Anxiety Treatments (CANMAT) conducted a revision of the 2009 guidelines by updating the evidence and recommendations. The scope of the 2016 guidelines remains the management of major depressive disorder (MDD) in adults, with a target audience of psychiatrists and other mental health professionals. Methods: Using the question-answer format, we conducted a systematic literature search focusing on systematic reviews and meta-analyses. Evidence was graded using CANMAT-defined criteria for level of evidence. Recommendations for lines of treatment were based on the quality of evidence and clinical expert consensus. “Complementary and Alternative Medicine Treatments” is the fifth of six sections of the 2016 guidelines. Results: Evidence-informed responses were developed for 12 questions for 2 broad categories of complementary and alternative medicine (CAM) interventions: 1) physical and meditative treatments (light therapy, sleep deprivation, exercise, yoga, and acupuncture) and 2) natural health products (St. John’s wort, omega-3 fatty acids; S-adenosyl-L-methionine [SAM-e], dehydroepiandrosterone, folate, Crocus sativus, and others). Recommendations were based on available data on efficacy, tolerability, and safety. Conclusions: For MDD of mild to moderate severity, exercise, light therapy, St. John’s wort, omega-3 fatty acids, SAM-e, and yoga are recommended as first- or second-line treatments. Adjunctive exercise and adjunctive St. John’s wort are second-line recommendations for moderate to severe MDD. Other physical treatments and natural health products have less evidence but may be considered as third-line treatments. CAM treatments are generally well tolerated. Caveats include methodological limitations of studies and paucity of data on long-term outcomes and drug interactions.

An admixture analysis of the age at index episodes in bipolar disorder

The interaction between polarity at onset (PAO) and age at onset (AAO) appears to be important for interpreting results of previous analyses of AAO in bipolar disorder (BD). Using an admixture analysis, we examined independently the distributions of age at first depressive and hypomanic/manic episodes in 379 BD I and II patients. Subsequently, we examined the association of PAO and AAO with specific clinical variables, using parametric and nonparametric analyses. Both depressive and manic onsets showed bimodal distributions. For depressive episodes, the means were: 18.5±4.1 (early onset) and 33.6±10.4 (late onset) years; and for manic episodes 18.9±3.3 (early onset) and 34.8±10.9 (late onset) years. For the overall AAO the best fit was for a mixture of three lognormal distributions (mean±S.D.): 15.5±2.0, 22.8±4.6, and 36.1±10.1years. Overall, an early onset was significantly associated with a chronic course of the disorder, a stronger family history of affective disorder, higher rates of rapid cycling, suicidal behavior, psychotic symptoms, and co-morbid anxiety disorders. Early onset depressive episodes were associated with higher rates of suicidal behavior and anxiety disorders, whereas early onset manic episodes were associated with psychotic symptoms and rapid cycling. Our results suggest the presence of a bimodal distribution of age at onset in BD according to the polarity of the index episode, and denote that an early onset BD, irrespective of polarity, may be a more serious subtype of the disorder.

Cross-prevalence of migraine and bipolar disorder

OBJECTIVE In two related studies, we explored the prevalence of migraine and its associated clinical characteristics in patients with bipolar disorder (BD) as well as psychiatric morbidity in patients treated for migraine. METHOD The first study included 323 subjects with BD type I (BD I) or BD type II (BD II), diagnosed using the Schedule for Affective Disorders and Schizophrenia, Lifetime version (SADS-L) format, or the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID). Migraine history was assessed by means of a structured questionnaire. In a second sample of 102 migraine patients, we investigated current and lifetime psychiatric morbidity using the SADS-L. Statistical analyses were conducted using nonparametric analysis and log-linear models. RESULTS A total of 24.5% of BD patients had comorbid migraine; those with BD II had a higher prevalence (34.8%) compared to BD I (19.1%) (p < 0.005). BD patients with comorbid migraine had significantly higher rates of suicidal behaviour, social phobia, panic disorder, generalized anxiety disorder, and obsessive-compulsive disorder (all p < 0.05). In the sample of migraine patients, 34.3% had a current psychiatric diagnosis, and 73.5% had a lifetime psychiatric diagnosis. The prevalence of BD I was 4.9%, and 7.8% for BD II. DISCUSSION Migraine is prevalent within the BD population, particularly among BD II subjects. It is associated with an increased risk of suicidal behaviour and comorbid anxiety disorders. Conversely, migraine sufferers have high rates of current and lifetime psychopathology. A greater understanding of this comorbidity may contribute to our knowledge of the underlying mechanisms of BD.

Nonlinear dynamics of mood regulation in bipolar disorder

We sought to study the underlying dynamic processes involved in mood regulation in subjects with bipolar disorder and healthy control subjects using time‐series analysis and to then analyze the relation between anxiety and mood using cross‐correlation techniques.

Early-onset and very-early-onset bipolar disorder: distinct or similar clinical conditions?

This study aimed to examine differences in the clinical presentation of very‐early‐onset (VEO) and early‐onset (EO) bipolar disorder (BD) not fully explored previously.

Exponential state transition dynamics in the rest–activity architecture of patients with bipolar disorder

Our goal was to model the temporal dynamics of sleep–wake transitions, represented by transitions between rest and activity obtained from actigraphic data, in patients with bipolar disorder using a probabilistic state transition approach.

Episode forecasting in bipolar disorder: Is energy better than mood?

Bipolar disorder is a severe mood disorder characterized by alternating episodes of mania and depression. Several interventions have been developed to decrease high admission rates and high suicides rates associated with the illness, including psychoeducation and early episode detection, with mixed results. More recently, machine learning approaches have been used to aid clinical diagnosis or to detect a particular clinical state; however, contradictory results arise from confusion around which of the several automatically generated data are the most contributory and useful to detect a particular clinical state. Our aim for this study was to apply machine learning techniques and nonlinear analyses to a physiological time series dataset in order to find the best predictor for forecasting episodes in mood disorders.

Electronic monitoring of self-reported mood: the return of the subjective?

This narrative review describes recent developments in the use of technology for utilizing the self-monitoring of mood, provides some relevant background, and suggests some promising directions. Subjective experience of mood is one of the valuable sources of information about the state of an integrated mind/brain system. During the past century, psychiatry and psychology moved away from subjectivity, emphasizing external observation, precise measurement, and laboratory techniques. This shift, however, provided only a limited improvement in the understanding of mood disorders, and it appears that self-monitoring of mood has the potential to enrich our knowledge, particularly when combined with the advances in technology. Modern technology, with its ability to transfer information from the individual directly to the researcher via electronic applications, enables us now to study mood regulation more thoroughly. Frequent subjective ratings can be helpful in identifying individualized treatment with effective mood stabilizers and recognizing subtypes of mood disorders. The variability of subjective ratings may also help us estimate the increased risk of recurrence and guide a tailored treatment.