The clinical characterization of the patient with primary psychosis aimed at personalization of management

The current management of patients with primary psychosis worldwide is often remarkably stereotyped. In almost all cases an antipsychotic medica­tion is prescribed, with second‐generation antipsychotics usually preferred to first‐generation ones. Cognitive behavioral therapy is rarely used in the vast majority of countries, although there is evidence to support its efficacy. Psychosocial interventions are often provided, especially in chronic cases, but those applied are frequently not validated by research. Evidence‐based family interventions and supported employment programs are seldom implemented in ordinary practice. Although the notion that patients with primary psychosis are at increased risk for cardiovascular diseases and diabetes mellitus is widely shared, it is not frequent that appropriate measures be implemented to address this problem. The view that the management of the patient with primary psychosis should be personalized is endorsed by the vast majority of clinicians, but this personalization is lacking or inadequate in most clinical contexts. Although many mental health services would declare themselves “recovery‐oriented”, it is not common that a focus on empowerment, identity, meaning and resilience is ensured in ordinary practice. The present paper aims to address this situation. It describes systematically the salient domains that should be considered in the characterization of the individual patient with primary psychosis aimed at personalization of management. These include positive and negative symptom dimensions, other psychopathological components, onset and course, neurocognition and social cognition, neurodevelopmental indicators; social functioning, quality of life and unmet needs; clinical staging, antecedent and concomitant psychiatric conditions, physical comorbidities, family history, history of obstetric complications, early and recent environmental exposures, protective factors and resilience, and internalized stigma. For each domain, simple assessment instruments are identified that could be considered for use in clinical practice and included in standardized decision tools. A management of primary psychosis is encouraged which takes into account all the available treatment modalities whose efficacy is supported by research evidence, selects and modulates them in the individual patient on the basis of the clinical characterization, addresses the patient’s needs in terms of employment, housing, self‐care, social relationships and education, and offers a focus on identity, meaning and resilience.     

Abandoning personalization to get to precision in the pharmacotherapy of depression

Effectiveness studies and analyses of naturalistic cohorts demonstrate that many patients with major depressive disorder do not experience symptomatic remission with antidepressant treatments. In an effort to better match patients with effective treatments, numerous investigations of predictors or moderators of treatment response have been reported over the past five decades, including clinical features as well as biological measures. However, none of these have entered routine clinical practice; instead, clinicians typically personalize treatment on the basis of patient preferences as well as their own. Here, we review the reasons why it has been challenging to identify and deploy treatment‐specific predictors of response, and suggest strategies that may be required to achieve true precision in the pharmacotherapy of depression. We emphasize the need for changes in how depression care is delivered, measured, and used to inform future practice.     

Effect of caloric restriction on depression

Recently, most of evidence shows that caloric restriction could induce antidepressant‐like effects in animal model of depression. Based on studies of the brain–gut axis, some signal pathways were common between the control of caloric restriction and depression. However, the specific mechanism of the antidepressant‐like effects induced by caloric restriction remains unclear. Therefore, in this article, we summarized clinical and experimental studies of caloric restriction on depression. This review may provide a new therapeutic strategy for depression.     

Proteomics and personalized medicine: a focus on kidney disease

ABSTRACT

Introduction: Inter-individual variability in response to drug treatment has induced an increased demand for decisions via personalize medicine. Also, the contribution of proteomics to the era of personalized medicine would seem to be vital in improving therapeutic outcomes.

Areas covered: We review validated biomarkers discovered by proteomics techniques and their use in personalized medicine with the focus on kidney diseases. We discuss this topic with a special emphasis on recent publications and relevant initiatives and depict some limitations that remain for personalized medicine.

Expert opinion: The development of highly accurate biomarkers is essential for optimizing the management of kidney diseases. Various biomarkers of kidney diseases have been identified using proteomic techniques. However, only a few of these biomarkers showed the potential to be used in clinical practice concerning personalized medicine. Therefore, it becomes evident that the combination of multiple biomarkers confers higher accuracy and the ability to depict complex pathophysiological conditions, a prerequisite for personalized treatment. CKD273, a multimarker panel for early CKD detection may serve as a first example for personalized medicine in nephrology. Based on this successful example, proteomics is expected to develop into the key technology to guide personalized intervention.     

Do we know enough? A scientific and ethical analysis of the basis for genetic-based personalized nutrition

This article discusses the prospects and limitations of the scientific basis for offering personalized nutrition advice based upon individual genetic information. Two divergent scientific positions are presented, with an ethical comment. The crucial question is whether the current knowledge base is sufficiently strong for taking an ethically responsible decision to offer personalized nutrition advice based upon gene–diet–health interaction. According to the first position, the evidence base for translating the outcomes of nutrigenomics research into personalized nutritional advice is as yet immature. There is also limited evidence that genotype-based dietary advice will motivate appropriate behavior changes. Filling the gaps in our knowledge will require larger and better randomized controlled trials. According to the second position, personalized nutrition must be evaluated in relation to generally accepted standard dietary advice—partly derived from epidemiological observations and usually not proven by clinical trials. With personalized nutrition, we cannot demand stronger evidence. In several specific cases of gene–diet interaction, it may be more beneficial for individuals with specific genotypes to follow personalized advice rather than general dietary recommendations. The ethical comment, finally, considers the ethical aspects of deciding how to proceed in the face of such uncertainty. Two approaches for an ethically responsible way forward are proposed. Arguing from a precautionary approach, it is suggested that personalized dietary advice should be offered only when there is strong scientific evidence for health effects, followed by stepwise evaluation of unforeseen behavioral and psychological effects. Arguing from theoretical and applied ethics as well as psychology, it is also suggested that personalized advice should avoid paternalism and instead focus on supporting the autonomous choice of each person.     

Behandlung depressiver Störungen bei Diabetes mellitus

Approximately 25% of all diabetes patients suffer from symptoms of clinical depression. This comorbidity of depression and diabetes is associated with hypoglycaemia, microvascular and microvascular complications and a clearly increased mortality. With regard to psychosocial outcome depression is related to impaired generic and diabetes-specific quality of life and poor treatment adherence. Despite this life endangering interaction depression is under-diagnosed and under-treated in diabetes patients. Therefore a screening for depression should be an integral part of routine care. Treatment for depression is aimed not only towards improvement of depression but should focus on physical aspects of diabetes as well. Depression can be treated with antidepressant medication, psychotherapy or a flexible combination of both. These approaches demonstrate relatively good results that are comparable to those patients with depression without diabetes. Up to now no single treatment that consistently leads to better medical outcome in patients with depression and diabetes could be identified. The management of diagnosis and treatment of comorbid depression in diabetes can be enhanced by following algorithms that are grounded on evidence-based treatment guidelines.     

Influence of body mass index on the choice of therapy for depression and follow‐up care

Objective: Overweight and obese patients commonly suffer from depression and choice of depression therapy may alter weight. We conducted a cohort study to investigate whether obesity is associated with treatment choices for depression; and whether obesity is associated with appropriate duration of depression treatment and receipt of follow‐up visits. Design and Methods: Adults with a diagnosis of depression between January 1, 2006 and March 31, 2010 who had 1+ new episodes of an antidepressant medication and/or psychotherapy were eligible. Medication use, encounters, diagnoses, height, and weight were collected from health plan databases. We modeled receipt of the different therapies (medication and psychotherapy) by BMI and BMI trajectory during the 9‐months prior to initiation of therapy using logistic regression models that accommodated correlation within provider and adjusted for covariates. We modeled BMI via a restricted cubic spline. Fluoxetine was the reference treatment option in the medication models. Results: Lower BMI was associated with greater use of mirtazapine, and a declining BMI prior to treatment was associated with greater odds of initiating mirtazapine and paroxetine. Higher BMI was associated with greater odds of initiating bupropion even after adjustment for smoking status. Obese patients were less likely to receive psychotherapy and less likely to receive appropriate duration (180‐days) of depression treatment compared to normal weight subjects. Conclusions: Our study provides evidence that BMI is considered when choosing therapy but associations were weak. Our results should prompt discussion about recommending and choosing depression treatment plans that optimize depression care and weight management concurrently. Differences in care and follow‐up by BMI warrant additional research.     

Antidepressants versus placebo in major depression: an overview

Although the early antidepressant trials which included severely ill and hospitalized patients showed substantial drug‐placebo differences, these robust differences have not held up in the trials of the past couple of decades, whether sponsored by pharmaceutical companies or non‐profit agencies. This narrowing of the drug‐placebo difference has been attributed to a number of changes in the conduct of clinical trials. First, the advent of DSM‐III and the broadening of the definition of major depression have led to the inclusion of mildly to moderately ill patients into antidepressant trials. These patients may experience a smaller magnitude of antidepressant‐placebo differences. Second, drug development regulators, such as the U.S. Food and Drug Administration and the European Medicines Agency, have had a significant, albeit underappreciated, role in determining how modern antidepressant clinical trials are designed and conducted. Their concerns about possible false positive results have led to trial designs that are poor, difficult to conduct, and complicated to analyze. Attempts at better design and patient selection for antidepressant trials have not yielded the expected results. As of now, antidepressant clinical trials have an effect size of 0.30, which, although similar to the effects of treatments for many other chronic illnesses, such as hypertension, asthma and diabetes, is less than impressive.     

Efficient probabilistic model personalization integrating uncertainty on data and parameters: Application to Eikonal-Diffusion models in cardiac electrophysiology

Biophysical models are increasingly used for medical applications at the organ scale. However, model predictions are rarely associated with a confidence measure although there are important sources of uncertainty in computational physiology methods. For instance, the sparsity and noise of the clinical data used to adjust the model parameters (personalization), and the difficulty in modeling accurately soft tissue physiology. The recent theoretical progresses in stochastic models make their use computationally tractable, but there is still a challenge in estimating patient-specific parameters with such models. In this work we propose an efficient Bayesian inference method for model personalization using polynomial chaos and compressed sensing. This method makes Bayesian inference feasible in real 3D modeling problems. We demonstrate our method on cardiac electrophysiology. We first present validation results on synthetic data, then we apply the proposed method to clinical data. We demonstrate how this can help in quantifying the impact of the data characteristics on the personalization (and thus prediction) results. Described method can be beneficial for the clinical use of personalized models as it explicitly takes into account the uncertainties on the data and the model parameters while still enabling simulations that can be used to optimize treatment. Such uncertainty handling can be pivotal for the proper use of modeling as a clinical tool, because there is a crucial requirement to know the confidence one can have in personalized models.     

Adding psychotherapy to antidepressant medication in depression and anxiety disorders: a meta‐analysis

We conducted a meta‐analysis of randomized trials in which the effects of treatment with antidepressant medication were compared to the effects of combined pharmacotherapy and psychotherapy in adults with a diagnosed depressive or anxiety disorder. A total of 52 studies (with 3,623 patients) met inclusion criteria, 32 on depressive disorders and 21 on anxiety disorders (one on both depressive and anxiety disorders). The overall difference between pharmacotherapy and combined treatment was Hedges' g = 0.43 (95% CI: 0.31‐0.56), indicating a moderately large effect and clinically meaningful difference in favor of combined treatment, which corresponds to a number needed to treat (NNT) of 4.20. There was sufficient evidence that combined treatment is superior for major depression, panic disorder, and obsessive‐compulsive disorder (OCD). The effects of combined treatment compared with placebo only were about twice as large as those of pharmacotherapy compared with placebo only, underscoring the clinical advantage of combined treatment. The results also suggest that the effects of pharmacotherapy and those of psychotherapy are largely independent from each other, with both contributing about equally to the effects of combined treatment. We conclude that combined treatment appears to be more effective than treatment with antidepressant medication alone in major depression, panic disorder, and OCD. These effects remain strong and significant up to two years after treatment. Monotherapy with psychotropic medication may not constitute optimal care for common mental disorders.     

The symptom‐specific efficacy of antidepressant medication vs. cognitive behavioral therapy in the treatment of depression: results from an individual patient data meta‐analysis

A recent individual patient data meta‐analysis showed that antidepressant medication is slightly more efficacious than cognitive behavioral therapy (CBT) in reducing overall depression severity in patients with a DSM‐defined depressive disorder. We used an update of that dataset, based on seventeen randomized clinical trials, to examine the comparative efficacy of antidepressant medication vs. CBT in more detail by focusing on individual depressive symptoms as assessed with the 17‐item Hamilton Rating Scale for Depression. Five symptoms (i.e., “depressed mood” , “feelings of guilt” , “suicidal thoughts” , “psychic anxiety” and “general somatic symptoms”) showed larger improvements in the medication compared to the CBT condition (effect sizes ranging from .13 to .16), whereas no differences were found for the twelve other symptoms. In addition, network estimation techniques revealed that all effects, except that on “depressed mood” , were direct and could not be explained by any of the other direct or indirect treatment effects. Exploratory analyses showed that information about the symptom‐specific efficacy could help in identifying those patients who, based on their pre‐treatment symptomatology, are likely to benefit more from antidepressant medication than from CBT (effect size of .30) versus those for whom both treatments are likely to be equally efficacious. Overall, our symptom‐oriented approach results in a more thorough evaluation of the efficacy of antidepressant medication over CBT and shows potential in “precision psychiatry” .     

抗抑郁新药的研究与开发

随着社会经济高速发展,人们面临的社会竞争和心理压力加大,导致抑郁症逐渐成为现代社会的常见及高发疾病,所以临床上对 抗抑郁新药的需求日益增长。概述近年来欧美上市的抗抑郁新药和目前临床III期在研的部分抗抑郁小分子药物的合成工艺及临床研究进 展。     

The severity of psychiatric disorders

The issue of the severity of psychiatric disorders has great clinical importance. For example, severity influences decisions about level of care, and affects decisions to seek government assistance due to psychiatric disability. Controversy exists as to the efficacy of antidepressants across the spectrum of depression severity, and whether patients with severe depression should be preferentially treated with medication rather than psychotherapy. Measures of severity are used to evaluate outcome in treatment studies and may be used as meaningful endpoints in clinical practice. But, what does it mean to say that someone has a severe illness? Does severity refer to the number of symptoms a patient is experiencing? To the intensity of the symptoms? To symptom frequency or persistence? To the impact of symptoms on functioning or on quality of life? To the likelihood of the illness resulting in permanent disability or death? Putting aside the issue of how severity should be operationalized, another consideration is whether severity should be conceptualized similarly for all illnesses or be disorder specific. In this paper, we examine how severity is characterized in research and contemporary psychiatric diagnostic systems, with a special focus on depression and personality disorders. Our review shows that the DSM‐5 has defined the severity of various disorders in different ways, and that researchers have adopted a myriad of ways of defining severity for both depression and personality disorders, although the severity of the former was predominantly defined according to scores on symptom rating scales, whereas the severity of the latter was often linked with impairments in functioning. Because the functional impact of symptom‐defined disorders depends on factors extrinsic to those disorders, such as self‐efficacy, resilience, coping ability, social support, cultural and social expectations, as well as the responsibilities related to one's primary role function and the availability of others to assume those responsibilities, we argue that the severity of such disorders should be defined independently from functional impairment.     

A neurocognitive model for understanding treatment action in depression

The way in which emotion is represented and processed in the human brain is an expanding area of research and has key implications for how we understand and potentially treat affective disorders such as depression. Characterizing the effects of pharmacological manipulations of key neurotransmitter systems can also help reveal the neurochemical underpinnings of emotional processing and how common antidepressant drugs may work in the treatment of depression and anxiety. This approach has revealed that depression is associated with both neural and behavioural biases towards negative over positive stimuli. Evidence from pharmacological challenge studies suggests that antidepressant treatment acts to normalize these biases early on in treatment, resulting in patients experiencing the world in a more positive way, improving their mood over time. This model is supported by evidence from both pharmacological and non-pharmacological interventions. The unique perspective on antidepressant treatment offered by this approach provides some insights into individual response to treatment, as well as novel approaches to drug development.     

Bringing 3D tumor models to the clinic – predictive value for personalized medicine

Current decision‐guiding algorithms in cancer drug treatment are based on decades of research and numerous clinical trials. For the majority of patients, this data is successfully applied for a systemic disease management. For a number of patients however, treatment stratification according to clinically based risk criteria will not be sufficient. The most effective treatment options are ideally identified prior to the start of clinical drug therapy. This review will discuss the implementation of three‐dimensional (3D) cell culture models as a preclinical testing paradigm for the efficacy of clinical cancer treatment. Patient tumor‐derived cells in 3D cultures duplicate the individual tumor microenvironment with a minimum of confounding factors. Clinical implementation of such personalized tumor models requires a high quality of methodological and clinical validation comparable to other biomarkers. A non‐systematic literature search demonstrated the small number of prospective studies that have been conducted in this area of research. This may explain the current reluctance of many physicians and insurance providers in implementing this type of assay into the clinical diagnostic routine despite potential benefit for patients. Achieving valid and reproducible results with a high level of evidence is central in improving the acceptance of preclinical 3D tumor models.     

Cell atrophy and loss in depression: reversal by antidepressant treatment

Depression is associated with structural alterations in limbic brain regions that control emotion and mood. Studies of chronic stress in animal models and postmortem tissue from depressed subjects demonstrate that these structural alterations result from atrophy and loss of neurons and glial cells. These findings indicate that depression and stress-related mood disorders can be considered mild neurodegenerative disorders. Importantly, there is evidence that these structural alterations can be blocked or even reversed by elimination of stress and by antidepressant treatments. A major focus of current investigations is to characterize the molecular signaling pathways and factors that underlie these effects of stress, depression, and antidepressant treatment. Recent advances in this research area are discussed and potential novel targets for antidepressant development are highlighted.     

Major Depression Predicts an Increase in Long‐Term Body Weight Variability in Young Adults

Objective: To test the hypothesis that major depression predicts an increase in long‐term body weight variability (BWV). Research Methods and Procedures: This was a prospective community‐based single‐age cohort study of young adults (N = 591) followed between the ages of 19 and 40. Following initial screening, information was derived from six subsequent semistructured diagnostic interviews conducted by mental health professionals. Major depression was diagnosed on the basis of DSM criteria. BWV was defined as the root mean square error of a regression line fitted to each individual's BMI values over time. Multiple regression analysis was used to test the association between major depression and BWV while controlling for potentially confounding variables including antidepressant treatment, eating disorder symptoms, and physical activity. We used random effects models to determine the temporal relationship between repeated measures of major depression and body weight change. Results: A highly significant positive association between major depression and BWV was found, whereas major depression was not associated with BMI level or BMI trend. Depression severity showed a dose‐response‐type relationship with the magnitude of BWV. After controlling for potentially confounding variables including antidepressant use, eating disorder symptoms, smoking, and physical activity, major depression remained a significant predictor of BWV (β= 0.13, p < 0.001). Longitudinal analysis revealed a unidirectional association between major depression and a later increase in body weight change rate irrespective of antidepressant medication. Discussion: Results from this study implicate depression as an important risk factor for increased BWV. Given increasing evidence for a link between major depression and both diabetes and cardiovascular disease, current results encourage further research on depression, BWV, and negative health outcomes.