Personalized medicine in major depressive disorder -- opportunities and pitfalls

Prognostic Significance of Blood-Based Baseline Biomarkers in Treatment-Resistant Depression: A Literature Review of Available Studies on Treatment Response

Major depressive disorder is a leading cause of disability worldwide and a major contributor to the overall global burden of disease. While there are several options for antidepressant treatment, only about 40–60% of patients respond to initial monotherapy, while 30–40% of patients may even show resistance to treatment. This article offers a narrative review of those studies evaluating the predictive properties of various blood-based baseline biomarkers regarding treatment responses to the pharmacological, stimulation, or behavioral treatment of patients with treatment-resistant depression (TRD). Our results show that overall, there is only a very limited number of studies assessing baseline peripheral biomarkers regarding treatment response in TRD. Although there is some evidence for the predictive significance of particular biomarkers (e.g., IL-6, CRP, BDNF), the majority of the results are either single-study reports or studies with conflicting results. This may contribute to the wide variety of treatment protocols and different TRD definition criteria, the small number of patients included, and the existence of different biological phenotypes of the disorder used within the various studies. Taken together, there does not yet appear to be any specific baseline peripheral biomarker with sufficient discriminative predictive validity that can be used in the routine clinical practice of TRD. The discovery of new biomarkers and the better clinical characterization of known biomarkers could support the better classification and staging of TRD, the development of personalized treatment algorithms with higher rates of remission and fewer side effects, and the development of new precision drugs for specific subgroups of patients.

Changes in inflammatory biomarkers are related to the antidepressant effects of Ayahuasca

BACKGROUND

Ayahuasca is a traditional Amazon brew and its potential antidepressant properties have recently been explored in scientific settings. We conducted a double-blind placebo-controlled trial of ayahuasca with treatment-resistant depression patients (n = 28) and healthy controls (n = 45).

AIMS

We are evaluating the blood inflammatory biomarkers: C-reactive protein and interleukin 6, as a potential consequence of ayahuasca intake and their correlation with serum cortisol and brain-derived neurotrophic factor levels. Blood samples were collected at pre-treatment and 48 hours after substance ingestion to assess the concentration of inflammatory biomarkers, together with administration of the Montgomery-Åsberg Depression Rating Scale.

RESULTS

At pre-treatment, patients showed higher C-reactive protein levels than healthy controls and a significant negative correlation between C-reactive protein and serum cortisol levels was revealed (rho = -0.40, n = 14). C-reactive protein in those patients was not correlated with Montgomery-Åsberg Depression Rating Scale scores. We observed a significant reduction of C-reactive protein levels across time in both patients and controls treated with ayahuasca, but not with placebo. Patients treated with ayahuasca showed a significant correlation (rho = + 0.57) between larger reductions of C-reactive protein and lower depressive symptoms at 48 hours after substance ingestion (Montgomery-Åsberg Depression Rating Scale). No significant result with respect to interleukin 6 and brain-derived neurotrophic factor was found. Furthermore, these biomarkers did not predict the antidepressant response or remission rates observed.

CONCLUSIONS

These findings enhance the understanding of the biological mechanisms behind the observed antidepressant effects of ayahuasca and encourage further clinical trials in adults with depression.

Toward a New Model of Understanding, Preventing, and Treating Adolescent Depression Focusing on Exhaustion and Stress

OBJECTIVE

Adolescent depression is a heterogeneous disorder, with a wide variety of symptoms and inconsistent treatment response, and is not completely understood. A dysregulated stress system is a consistent finding, however, and exhaustion is a consistent trait in adolescent patients. The aim of this paper is to critically assess current hypotheses in adolescent depression research and reframe causes and treatment approaches.

METHODS

A mixed-method approach involved a review based on publications from PubMed, Embase and PsycInfo, and two exemplary adolescent cases.

RESULTS

Both cases show a spiral of stress and exhaustion, but with a different profile of symptoms and coping mechanisms. Reframing both cases from the perspective of coping behavior, searching for the sources of experienced stress and exhaustion, showed coping similarities. This proved essential in the successful personalized treatment and recovery process. In combination with recent evidence, both cases support the functional reframing of depression as the outcome of a stress- and exhaustion-related spiralling mechanism.

CONCLUSIONS

We propose to open up a symptom-based, mood-centered view to a model in which adolescent depression is framed as a consecutive failure of stress coping mechanisms and chronic exhaustion. Addressing exhaustion and coping primarily as a treatment strategy in adolescents and young adults might work in synergy with existing treatments and improve overall outcomes. This perspective warrants further investigation.

Depressive disorders: Treatment failures and poor prognosis over the last 50 years

Depression like many diseases is pleiotropic but unlike cancer and Alzheimer's disease for example, is still largely stigmatized and falls into the dark shadows of human illness. The failure of depression to be in the spotlight for successful treatment options is inherent in the complexity of the disease(s), flawed clinical diagnosis, overgeneralization of the illness, inadequate and biased clinical trial design, restrictive and biased inclusion/exclusion criteria, lack of approved/robust biomarkers, expensive imaging technology along with few advances in neurobiological hypotheses in decades. Clinical trial studies summitted to the regulatory agencies (FDA/EMA) for approval, have continually failed to show significant differences between active and placebo. For decades, we have acknowledged this failure, despite vigorous debated by all stakeholders to provide adequate answers to this escalating problem, with only a few new antidepressants approved in the last 20 years with equivocal efficacy, little improvement in side effects or onset of efficacy. It is also clear that funding and initiatives for mental illness lags far behind other life-treating diseases. Thus, it is no surprise we have not achieved much success in the last 50 years in treating depression, but we are accountable for the many failures and suboptimal treatment. This review will therefore critically address where we have failed and how future advances in medical science offers a glimmer of light for the patient and aid our future understanding of the neurobiology and pathophysiology of the disease, enabling transformative therapies for the treatment of depressive disorders.

Data-driven biological subtypes of depression: systematic review of biological approaches to depression subtyping

Research into major depressive disorder (MDD) is complicated by population heterogeneity, which has motivated the search for more homogeneous subtypes through data-driven computational methods to identify patterns in data. In addition, data on biological differences could play an important role in identifying clinically useful subtypes. This systematic review aimed to summarize evidence for biological subtypes of MDD from data-driven studies. We undertook a systematic literature search of PubMed, PsycINFO, and Embase (December 2018). We included studies that identified (1) data-driven subtypes of MDD based on biological variables, or (2) data-driven subtypes based on clinical features (e.g., symptom patterns) and validated these with biological variables post-hoc. Twenty-nine publications including 24 separate analyses in 20 unique samples were identified, including a total of ~ 4000 subjects. Five out of six biochemical studies indicated that there might be depression subtypes with and without disturbed neurotransmitter levels, and one indicated there might be an inflammatory subtype. Seven symptom-based studies identified subtypes, which were mainly determined by severity and by weight gain vs. loss. Two studies compared subtypes based on medication response. These symptom-based subtypes were associated with differences in biomarker profiles and functional connectivity, but results have not sufficiently been replicated. Four out of five neuroimaging studies found evidence for groups with structural and connectivity differences, but results were inconsistent. The single genetic study found a subtype with a distinct pattern of SNPs, but this subtype has not been replicated in an independent test sample. One study combining all aforementioned types of data discovered a subtypes with different levels of functional connectivity, childhood abuse, and treatment response, but the sample size was small. Although the reviewed work provides many leads for future research, the methodological differences across studies and lack of replication preclude definitive conclusions about the existence of clinically useful and generalizable biological subtypes.

Biomarker-based subtyping of depression and anxiety disorders using Latent Class Analysis. A NESDA study

BACKGROUND

Etiological research of depression and anxiety disorders has been hampered by diagnostic heterogeneity. In order to address this, researchers have tried to identify more homogeneous patient subgroups. This work has predominantly focused on explaining interpersonal heterogeneity based on clinical features (i.e. symptom profiles). However, to explain interpersonal variations in underlying pathophysiological mechanisms, it might be more effective to take biological heterogeneity as the point of departure when trying to identify subgroups. Therefore, this study aimed to identify data-driven subgroups of patients based on biomarker profiles.

METHODS

Data of patients with a current depressive and/or anxiety disorder came from the Netherlands Study of Depression and Anxiety, a large, multi-site naturalistic cohort study (n = 1460). Thirty-six biomarkers (e.g. leptin, brain-derived neurotrophic factor, tryptophan) were measured, as well as sociodemographic and clinical characteristics. Latent class analysis of the discretized (lower 10%, middle, upper 10%) biomarkers were used to identify different patient clusters.

RESULTS

The analyses resulted in three classes, which were primarily characterized by different levels of metabolic health: 'lean' (21.6%), 'average' (62.2%) and 'overweight' (16.2%). Inspection of the classes' clinical features showed the highest levels of psychopathology, severity and medication use in the overweight class.

CONCLUSIONS

The identified classes were strongly tied to general (metabolic) health, and did not reflect any natural cutoffs along the lines of the traditional diagnostic classifications. Our analyses suggested that especially poor metabolic health could be seen as a distal marker for depression and anxiety, suggesting a relationship between the 'overweight' subtype and internalizing psychopathology.

Prophylactic ketamine alters nucleotide and neurotransmitter metabolism in brain and plasma following stress

Recently, we have shown that ketamine given prior to stress exposure protects against the development of depressive-like behavior in mice. These data suggest that it may be possible to prevent the induction of affective disorders before they develop by administering prophylactic pharmaceuticals, a relatively nascent and unexplored strategy for psychiatry. Here, we performed metabolomics analysis of brain and plasma following prophylactic ketamine treatment in order to identify markers of stress resilience enhancement. We administered prophylactic ketamine in mice to buffer against fear expression. Following behavioral analyses, untargeted metabolomic profiling was performed on both hemispheres of the prefrontal cortex (PFC) and the hippocampus (HPC), and plasma. We found that prophylactic ketamine attenuated learned fear. Eight metabolites were changed in the PFC and HPC upon ketamine treatment. Purine and pyrimidine metabolism were most significantly changed in the HPC, PFC, and, interestingly, plasma of mice two weeks after prophylactic administration. Moreover, most precursors to inhibitory neurotransmitters were increased whereas precursors to excitatory neurotransmitters were decreased. Strikingly, these long-term metabolomic changes were not observed when no stressor was administered. Our results suggest that prophylactic treatment differentially affects purine and pyrimidine metabolism and neurotransmission in brain and plasma following stress, which may underlie the long-lasting resilience to stress induced by a single injection of ketamine. These data may provide novel targets for prophylactic development, and indicate an interaction effect of prophylactic ketamine and stress. To our knowledge, this is the first study that identifies metabolomic alterations and biomarker candidates for prophylactic ketamine efficacy in mice.

Hippocampal metabolic differences implicate distinctions between physical and psychological stress in four rat models of depression

Major depressive disorder (MDD) is a heterogeneous and multi-factorial disorder, and the underlying molecular mechanisms remain largely unknown. However, many studies have indicated that the molecular mechanisms underlying depression in response to different stress may differ. After screening, 28-30 rats were included in each model of depression (chronic unpredictable mild stress (CUMS); learned helplessness (LH); chronic restraint stress (CRS); or social defeat (SD)). Non-targeted gas chromatography-mass spectrometry was used to profile the metabolic changes in the hippocampus. As a result, all four models exhibited significant depression-like behavior. A total of 30, 24, 19, and 25 differential metabolites were identified in the CUMS, LH, CRS, and SD models, respectively. Interestingly, the hierarchical clustering results revealed two patterns of metabolic changes that are characteristic of the response to cluster 1 (CUMS, LH) and cluster 2 (CRS, SD) stress, which represent physical and psychological stress, respectively. Bioinformatic analysis suggested that physical stress was mainly associated with lipid metabolism and glutamate metabolism, whereas psychological stress was related to cell signaling, cellular proliferation, and neurodevelopment, suggesting the molecular changes induced by physical and psychological stress were different. Nine shared metabolites were opposite in the directions of change between physical and psychological models, and these metabolites were associated with cellular proliferation and neurodevelopment functions, indicating the response to physical and psychological stress was different in the activation and deactivation of the final common pathway to depression. Our results provide a further understanding of the heterogeneity in the molecular mechanisms of MDD that could facilitate the development of personalized medicine for this disorder.

Exploring venlafaxine pharmacokinetic variability with a phenotyping approach, a multicentric french-swiss study (MARVEL study)

Background

It is well known that the standard doses of a given drug may not have equivalent effects in all patients. To date, the management of depression remains mainly empirical and often poorly evaluated. The development of a personalized medicine in psychiatry may reduce treatment failure, intolerance or resistance, and hence the burden and costs of mood depressive disorders.

The Geneva Cocktail Phenotypic approach presents several advantages including the “in vivo” measure of different cytochromes and transporter P-gp activities, their simultaneous determination in a single test, avoiding the influence of variability over time on phenotyping results, the administration of low dose substrates, a limited sampling strategy with an analytical method developed on DBS analysis.

The goal of this project is to explore the relationship between the activity of drug-metabolizing enzymes (DME), assessed by a phenotypic approach, and the concentrations of Venlafaxine (VLX) + O-demethyl-venlafaxine (ODV), the efficacy and tolerance of VLX.

Methods/design

This study is a multicentre prospective non-randomized open trial. Eligible patients present a major depressive episode, MADRS over or equal to 20, treatment with VLX regardless of the dose during at least 4 weeks. The Phenotype Visit includes VLX and ODV concentration measurement.

Following the oral absorption of low doses of omeprazole, midazolam, dextromethorphan, and fexofenadine, drug metabolizing enzymes activity is assessed by specific metabolite/probe concentration ratios from a sample taken 2 h after cocktail administration for CYP2C19, CYP3A4, CYP2D6; and by the determination of the limited area under the curve from the capillary blood samples taken 2–3 and 6 h after cocktail administration for CYP2C19 and P-gp.

Two follow-up visits will take place between 25 and 40 days and 50–70 days after inclusion. They include assessment of efficacy, tolerance and observance.

Eleven french centres are involved in recruitment, expected to be completed within approximately 2 years with 205 patients. Metabolic ratios are determined in Geneva, Switzerland.

Discussion

By showing an association between drug metabolism and VLX concentrations, efficacy and tolerance, there is a hope that testing drug metabolism pathways with a phenotypical approach would help physicians in selecting and dosing antidepressants. The MARVEL study will provide an important contribution to increasing the knowledge of VLX variability and in optimizing the use of methods of personalized therapy in psychiatric settings.

Trial registration

ClinicalTrials.govNCT02590185 (10/27/2015). This study is currently recruiting participants.

Optimization of Antidepressant use with Pharmacogenetic Strategies

BACKGROUND

The response rate in the pharmacological treatment of depression has been estimated to be around 50%, achieving a remission in symptomatology in only one third of the patients. Suboptimal prescription of antidepressants has been proposed as a significant explanatory factor for this therapeutic inefficacy. The use of pharmacogenetic testing might favor the optimization of pharmacotherapy in emotional disorders. However, its implementation in the clinical routine requires studies which prove its efficacy.

OBJECTIVE

The aim is to explore the clinical effects obtained by means of the personalization of antidepressant treatment derived from the pharmacogenetic profile of the individual.

METHOD

A sample of 291 patients under antidepressant treatment was selected, and these patients were genotyped for the most common polymorphisms of the CYP2D6, CYP2C9, CYP2C19 and CYP3A4/5 genes using RT-PCR and TaqMan® technology. 30 of them were subjected to psycho-affective assessment using the HDRS scale before and after a process of individualization of their psychopharmacological treatment in accordance with the genotype obtained.

RESULTS

70% of the individuals treated using the traditional criterion of trial-and-error were not taking the active ingredient most suited to their pharmacogenetic profile. The inclusion of this genetic information in the choice of drug and its dosage entailed a significant, progressive reduction in depressive symptomatology, with an efficacy ratio of 80% and a remission of the pathology in almost 30% of the cases.

CONCLUSION

These results suggest that the prescription of pharmacogenetic profile-based strategies has a positive effect on the therapeutic response to antidepressants.

Optimization of Antidepressant use with Pharmacogenetic Strategies

Background:

The response rate in the pharmacological treatment of depression has been estimated to be around 50%, achieving a remission in symptomatology in only one third of the patients. Suboptimal prescription of antidepressants has been proposed as a significant explanatory factor for this therapeutic inefficacy. The use of pharmacogenetic testing might favor the optimization of pharmacotherapy in emotional disorders. However, its implementation in the clinical routine requires studies which prove its efficacy.

Objective:

The aim is to explore the clinical effects obtained by means of the personalization of antidepressant treatment derived from the pharmacogenetic profile of the individual.

Method:

A sample of 291 patients under antidepressant treatment was selected, and these patients were genotyped for the most common polymorphisms of the CYP2D6, CYP2C9, CYP2C19 and CYP3A4/5 genes using RT-PCR and TaqMan® technology. 30 of them were subjected to psycho-affective assessment using the HDRS scale before and after a process of individualization of their psychopharmacological treatment in accordance with the genotype obtained.

Results:

70% of the individuals treated using the traditional criterion of trial-and-error were not taking the active ingredient most suited to their pharmacogenetic profile. The inclusion of this genetic information in the choice of drug and its dosage entailed a significant, progressive reduction in depressive symptomatology, with an efficacy ratio of 80% and a remission of the pathology in almost 30% of the cases.

Conclusion:

These results suggest that the prescription of pharmacogenetic profile-based strategies has a positive effect on the therapeutic response to antidepressants.

Blood-based immune-endocrine biomarkers of treatment response in depression

Antidepressant treatment for major depressive disorder remains suboptimal with response rates of just over 50%. Although treatment guidelines, algorithms and clinical keys are available to assist the clinician, the process of finding an effective pharmacotherapy to maximise benefit for the individual patient is largely by "trial and error" and remains challenging. This highlights a clear need to identify biomarkers of treatment response to help guide personalised treatment strategies. We have carried out the largest multiplex immunoassay based longitudinal study to date, examining up to 258 serum markers involved in immune, endocrine and metabolic processes as potential biomarkers associated with treatment response in 332 depression patients recruited from four independent clinical centres. We demonstrated for the first time that circulating Apolipoprotein A-IV, Endoglin, Intercellular Adhesion Molecule 1, Tissue Inhibitor of Metalloproteinases 1, Plasminogen Activator Inhibitor 1, Thrombopoietin, Complement C3, Hepatocyte Growth Factor and Insulin-like Growth Factor-Binding Protein 2 were associated with response to different antidepressants. In addition, we showed that specific sets of immune-endocrine proteins were associated with response to Venlafaxine (serotonin-norepinephrine reuptake inhibitor), Imipramine (tricyclic antidepressant) and other antidepressant drugs. However, we were not able to reproduce the literature findings on BDNF and TNF-α, two of the most commonly reported candidate treatment response markers. Despite the need for extensive validation studies, our preliminary findings suggest that a pre-treatment immune-endocrine profile may help to determine a patient's likelihood to respond to specific antidepressant and/or alternative treatments such as anti-inflammatory drugs, providing hope for future personalised treatment approaches.

Pharmacogenomics and Global Precision Medicine in the Context of Adverse Drug Reactions: Top 10 Opportunities and Challenges for the Next Decade

In a move indicative of the enthusiastic support of precision medicine, the U.S. President Barack Obama announced the Precision Medicine Initiative in January 2015. The global precision medicine ecosystem is, thus, receiving generous support from the United States ($215 million), and numerous other governments have followed suit. In the context of precision medicine, drug treatment and prediction of its outcomes have been important for nearly six decades in the field of pharmacogenomics. The field offers an elegant solution for minimizing the effects and occurrence of adverse drug reactions (ADRs). The Clinical Pharmacogenetics Implementation Consortium (CPIC) plays an important role in this context, and it aims at specifically guiding the translation of clinically relevant and evidence-based pharmacogenomics research. In this forward-looking analysis, we make particular reference to several of the CPIC guidelines and their role in guiding the treatment of highly relevant diseases, namely cardiovascular disease, major depressive disorder, cancer, and human immunodeficiency virus, with a view to predicting and managing ADRs. In addition, we provide a list of the top 10 crosscutting opportunities and challenges facing the fields of precision medicine and pharmacogenomics, which have broad applicability independent of the drug class involved. Many of these opportunities and challenges pertain to infrastructure, study design, policy, and science culture in the early 21st century. Ultimately, rational pharmacogenomics study design and the acquisition of comprehensive phenotypic data that proportionately match the genomics data should be an imperative as we move forward toward global precision medicine.

Amygdala response to explicit sad face stimuli at baseline predicts antidepressant treatment response to scopolamine in major depressive disorder

The muscarinic antagonist scopolamine produces rapid antidepressant effects in individuals with major depressive disorder (MDD). In healthy subjects, manipulation of acetyl-cholinergic transmission modulates attention in a stimulus-dependent manner. This study tested the hypothesis that baseline amygdalar activity in response to emotional stimuli correlates with antidepressant treatment response to scopolamine and could thus potentially predict treatment outcome. MDD patients and healthy controls performed an attention shifting task involving emotional faces while undergoing functional magnetic resonance imaging (fMRI). We found that blood oxygenation level dependent (BOLD) signal in the amygdala acquired while MDD patients processed sad face stimuli correlated positively with antidepressant response to scopolamine. Amygdalar response to sad faces in MDD patients who did not respond to scopolamine did not differ from that of healthy controls. This suggests that the pre-treatment task elicited amygdalar activity that may constitute a biomarker of antidepressant treatment response to scopolamine. Furthermore, in MDD patients who responded to scopolamine, we observed a post-scopolamine stimulus processing shift towards a pattern demonstrated by healthy controls, indicating a change in stimulus-dependent neural response potentially driven by attenuated cholinergic activity in the amygdala.

Longitudinal monitoring of the serotonin transporter gene expression to assess major depressive episode evolution

BACKGROUND

Mood disorders are frequently characterized by uncertain prognosis and studying mRNA expression variations in blood cells represents a promising avenue of identifying biomarkers for mood disorders. State-dependent gene expression variations have been described during a major depressive episode (MDE), in particular for SLC6A4 mRNA, but how this transcript varies in relation to MDE evolution remains unclear. In this study, we prospectively assessed time trends of SCL6A4 mRNA expression in responder and nonresponder patients.

METHODS

We examined SLC6A4 mRNA expression in blood samples from 13 patients treated for severe MDE and their matched controls by reverse transcription and quantitative PCR. All subjects were followed for 30 weeks. Patients were classified as either responders or nonresponders based on improvement of depression according to the 17-item Hamilton Depression Rating Scale. Using a longitudinal design, we ascertained mRNA expression at baseline, 2, 8, and 30 weeks and compared mRNA expression between responder and nonresponder patients, and matched controls.

RESULTS

We observed a decrease of SLC6A4 mRNA expression in responder patients across a 30-week follow-up, while nonresponder patients exhibited up-regulated SLC6A4 mRNA.

CONCLUSION

Peripheral SLC6A4 mRNA expression could serve as a biomarker for monitoring and follow-up during an MDE and may help to more appropriately select individualized treatments.

Genome-wide linkage on chromosome 10q26 for a dimensional scale of major depression

Major depressive disorder (MDD) is a common and potentially life-threatening mood disorder. Identifying genetic markers for depression might provide reliable indicators of depression risk, which would, in turn, substantially improve detection, enabling earlier and more effective treatment. The aim of this study was to identify rare variants for depression, modeled as a continuous trait, using linkage and post-hoc association analysis. The sample comprised 1221 Mexican-American individuals from extended pedigrees. A single dimensional scale of MDD was derived using confirmatory factor analysis applied to all items from the Past Major Depressive Episode section of the Mini-International Neuropsychiatric Interview. Scores on this scale of depression were subjected to linkage analysis followed by QTL region-specific association analysis. Linkage analysis revealed a single genome-wide significant QTL (LOD=3.43) on 10q26.13, QTL-specific association analysis conducted in the entire sample revealed a suggestive variant within an intron of the gene LHPP (rs11245316, p=7.8×10(-04); LD-adjusted Bonferroni-corrected p=8.6×10(-05)). This region of the genome has previously been implicated in the etiology of MDD; the present study extends our understanding of the involvement of this region by highlighting a putative gene of interest (LHPP).

Association of Periodontitis and Subsequent Depression: A Nationwide Population-Based Study

Periodontitis is a systemic and chronic inflammatory disease associated with multiple physical conditions. Distress and depression are other problems affecting the progression of periodontitis. However, the causal relationship between depression and periodontitis has not been adequately investigated. This aim of this study was to determine the association between periodontitis and the subsequent development of depression.We identified 12,708 patients with newly diagnosed periodontitis from 2000 to 2005 and 50,832 frequency-matched individuals without periodontitis. Both groups were followed until diagnosed with depression, withdrawal from the National Health Insurance program, or the end of 2011. The association between periodontitis and depressio was analyzed using Cox proportional hazard regression models.The incidence density rate of depression was higher in the periodontitis group than in the nonperiodontitis group, with an adjusted hazard ratio of 1.73 (95% confidence interval 1.58-1.89) when adjusting for sex, age, and comorbidity. Cox models revealed that periodontitis was an independent risk factor for depression in patients, except for comorbidities of diabetes mellitus (DM), alcohol abuse, and cancer.Periodontitis may increase the risk of subsequent depression and was suggested an independent risk factor regardless of sex, age, and most comorbidities. However, DM, alcohol abuse, and cancer may prevent the development of subsequent depression because of DM treatment, the paradoxical effect of alcohol, and emotional distress to cancer, respectively. Prospective studies on the relationship between periodontitis and depression are warranted.

Rodent models of depression: neurotrophic and neuroinflammatory biomarkers

Rodent models are an indispensable tool for studying etiology and progress of depression. Since interrelated systems of neurotrophic factors and cytokines comprise major regulatory mechanisms controlling normal brain plasticity, impairments of these systems form the basis for development of cerebral pathologies, including mental diseases. The present review focuses on the numerous experimental rodent models of depression induced by different stress factors (exteroceptive and interoceptive) during early life (including prenatal period) or adulthood, giving emphasis to the data on the changes of neurotrophic factors and neuroinflammatory indices in the brain. These parameters are closely related to behavioral depression-like symptoms and impairments of neuronal plasticity and are both gender- and genotype-dependent. Stress-related changes in expression of neurotrophins and cytokines in rodent brain are region-specific. Some contradictory data reported by different groups may be a consequence of differences of stress paradigms or their realization in different laboratories. Like all experimental models, stress-induced depression-like conditions are experimental simplification of clinical depression states; however, they are suitable for understanding the involvement of neurotrophic factors and cytokines in the pathogenesis of the disease—a goal unachievable in the clinical reality. These major regulatory systems may be important targets for therapeutic measures as well as for development of drugs for treatment of depression states.

Hands-on workshops as an effective means of learning advanced technologies including genomics, proteomics and bioinformatics

Genomics and proteomics have emerged as key technologies in biomedical research, resulting in a surge of interest in training by investigators keen to incorporate these technologies into their research. At least two types of training can be envisioned in order to produce meaningful results, quality publications and successful grant applications: (1) immediate short-term training workshops and (2) long-term graduate education or visiting scientist programs. We aimed to fill the former need by providing a comprehensive hands-on training course in genomics, proteomics and informatics in a coherent, experimentally-based framework. This was accomplished through a National Heart, Lung, and Blood Institute (NHLBI)-sponsored 10-day Genomics and Proteomics Hands-on Workshop held at National Jewish Health (NJH) and the University of Colorado School of Medicine (UCD). The course content included comprehensive lectures and laboratories in mass spectrometry and genomics technologies, extensive hands-on experience with instrumentation and software, video demonstrations, optional workshops, online sessions, invited keynote speakers, and local and national guest faculty. Here we describe the detailed curriculum and present the results of short- and long-term evaluations from course attendees. Our educational program consistently received positive reviews from participants and had a substantial impact on grant writing and review, manuscript submissions and publications.

Concerns about unintended negative consequences of informing the public about multifactorial risks may be premature for young adult smokers

BACKGROUND

Many health risks are associated with both genetic and behavioural factors. Concerns have been raised that learning about such multifactorial risks might have detrimental effects on health-related beliefs, cognitions, and affect. However, experimental evidence is sparse.

OBJECTIVE

To explore the effects of reading an online news article about the discovery of a genetic basis for nicotine addiction.

METHODS

Smokers (N = 333) were recruited from the psychology subject pools of two major universities. Participants were randomly assigned to read one of three news articles: one describing a genetic basis for nicotine addiction and lung cancer obtained from a national news source, one altered to indicate no genetic basis for nicotine addiction and lung cancer, or one unrelated attention control. Participants then completed an online questionnaire, which assessed smoking-related cognitions and affect, and beliefs about nicotine addiction, quitting smoking, and whether the harms of tobacco use are delayed.

RESULTS

There was no statistically significant influence of experimental condition on smoking-related cognitions/affect (ps > .05, η(2)  < .002), beliefs about addiction and quitting (Wilks' λ = .98, p = .66, η(2)  = .01), or delayed harm (ps > .05, η(2)  < .002).

CONCLUSION

Reading an online news article about the presence or absence of a genetic basis for nicotine addiction was not found to change smoking-related cognitions/affect or beliefs among young adult smokers. Concerns about negative effects of multifactorial risk information on health beliefs may be premature. Nevertheless, to effectively translate basic genomics research into effective public health practice, further research should investigate these issues in different populations, via different communication modalities, and for different health outcomes.

STATEMENT OF CONTRIBUTION

What is already known on this subject? Information about the health implications of the interaction between genetics and behaviour is becoming prevalent. Learning about these interactions may reduce perceived risk and intentions to engage in health behaviours. What does this study add? Informing young adult smokers about the genetic basis for nicotine addiction does not affect health beliefs negatively. Responses are not moderated by endorsing the idea of genetic causation or current/experimenter smoking status.