The impact of nutrition on psycho-affective status in an older Cretan population: a cross-sectional study

BACKGROUND

Cognitive and mood status influence both personal and social daily activities, with great impact on life quality, particularly among the elderly population.

AIM

This cross-sectional study aimed to investigate the psycho-affective status concerning eating habits within an elderly population of the Chania area in Crete, Greece.

METHODS

Cognitive status was assessed in 101 elderly subjects through the Mini-Mental State Examination (MMSE), and mood was evaluated using the Hospital Anxiety and Depression Scale (HADS). Nutritional status was assessed using a validated food frequency questionnaire.

RESULTS

Multivariable statistical analysis, after adjustment for age, marital status, education, and comorbidity, highlighted among males a positive association of the MMSE score with vegetable consumption (RR 1.18; 95%CI 1.03‒1.34) and a negative association with potato consumption (RR 0.83; 95%CI 0.72‒0.95). Conversely, among females, no statistically significant association was observed for any food. Further, among males, a protective effect on affective status was identified for chicken meat (RR 0.45; 95%CI 0.27‒0.77), fish (RR 0.41; 95%CI 0.21‒0.82), fruit (RR 0.70; 95%CI 0.52‒0.94), cereals (RR 0.67; 95%CI 0.53‒0.87), and cheese (RR 0.78; 95%CI 0.63‒0.97) consumption. Among females, the adjusted model showed a significant detrimental effect of vegetable consumption (RR 1.33; 95%CI 1.02‒1.73).

CONCLUSION

A predominantly vegetable-based diet-with the notable exception of fruits and legumes-was associated with better cognitive status in males, albeit not in females. A higher intake of fruit, as well as fish, chicken meat, and cheese among males was associated with a better affective status, indicating that adequate protein supply may play a role in maintaining emotional balance.

Repurposing of pexidartinib for microglia depletion and renewal

Pexidartinib (PLX3397) is a small molecule receptor tyrosine kinase inhibitor of colony stimulating factor 1 receptor (CSF1R) with moderate selectivity over other members of the platelet derived growth factor receptor family. It is approved for treatment of tenosynovial giant cell tumors (TGCT). CSF1R is highly expressed by microglia, which are macrophages of the central nervous system (CNS) that defend the CNS against injury and pathogens and contribute to synapse development and plasticity. Challenged by pathogens, apoptotic cells, debris, or inflammatory molecules they adopt a responsive state to propagate the inflammation and eventually return to a homeostatic state. The phenotypic switch may fail, and disease-associated microglia contribute to the pathophysiology in neurodegenerative or neuropsychiatric diseases or long-lasting detrimental brain inflammation after brain, spinal cord or nerve injury or ischemia/hemorrhage. Microglia also contribute to the growth permissive tumor microenvironment of glioblastoma (GBM). In rodents, continuous treatment for 1-2 weeks via pexidartinib food pellets leads to a depletion of microglia and subsequent repopulation from the remaining fraction, which is aided by peripheral monocytes that search empty niches for engraftment. The putative therapeutic benefit of such microglia depletion or forced renewal has been assessed in almost any rodent model of CNS disease or injury or GBM with heterogeneous outcomes, but a tendency of partial beneficial effects. So far, microglia monitoring e.g. via positron emission imaging is not standard of care for patients receiving Pexidartinib (e.g. for TGCT), so that the depletion and repopulation efficiency in humans is still largely unknown. Considering the virtuous functions of microglia, continuous depletion is likely no therapeutic option but short-lasting transient partial depletion to stimulate microglia renewal or replace microglia in genetic disease in combination with e.g. stem cell transplantation or as part of a multimodal concept in treatment of glioblastoma appears feasible. The present review provides an overview of the preclinical evidence pro and contra microglia depletion as a therapeutic approach.

The use of biochemical indexes in hair for clinical studies of psychiatric diseases: What can we learn about mental disease from hair?

Analysis of hair samples provides unique advantages, including non-invasive sampling, sample stability, and the possibility of additional optimization of high sensitivity detection methods. Hair sample analysis is often used in psychiatric disease research to evaluate previous periods of stress encountered by patients. Glucocorticoid analysis is the most frequently tested indicator of stress. Furthermore, the hypothalamus-pituitary-gonad axis and endocannabinoid system also are involved in the occurrence and development of mental disorders. The endocannabinoid and sex hormone levels in patients experiencing mental illness are considerably different from levels observed in healthy individuals. Nevertheless, due to the different methods used to assess the degree of disease and the range of analytical methods involved in clinical research, the trends in changes for these biomarkers are not uniform. The correlations between changes in biomarker concentrations and illness severity also are not clear. The observed alterations suggest these biochemical substances in hair have potential as biomarkers for diagnosis or predictive treatment. However, the variable results obtained thus far could hamper further development of hair samples for clinical assessment in psychiatric disorders. This article summarizes the published reports documenting the changes in the content of relevant substances in hair in individuals experiencing mental illness and the degree of correlation. In the discussion section, we proposed several issues that should be considered in future studies of hair samples obtained from patients with mental disorders to promote the use of hair sample assessment as an aid in diagnosis or predictive treatment.

Progress on the application of positron emission tomography imaging of cannabinoid type 1 receptor in neuropsychiatric diseases

Cannabinoid type 1 receptor (CB1R), as the major member of the endocannabinoid system, is among the most abundant receptors expressed in the central nervous system. CB1R is mainly located on the axon terminals of presynaptic neurons and participate in the modulation of neuronal excitability and synaptic plasticity, playing an important role in the pathogenesis of various neuropsychiatric diseases. In recent years, the consistent development of CB1R radioligands and the maturity of molecular imaging techniques, particularly positron emission tomography (PET) may help to visualize the expression and distribution of CB1R in central nervous system . At present, CB1R PET imaging can effectively evaluate the changes of CB1R levels in neuropsychiatric diseases such as Huntington's disease and schizophrenia, and its correlation with the disease severity, therefore providing new insights for the diagnosis and treatment of neuropsychiatric diseases. This article reviews the application of CB1R PET imaging in Alzheimer's disease, Parkinson's disease, Huntington's disease, schizophrenia, post-traumatic stress disorder, cannabis use disorder and depression.

The thermodynamic brain and the evolution of intellect: the role of mental energy

The living state is low entropy, highly complex organization, yet it is part of the energy cycle of the environment. Due to the recurring presence of the resting state, stimulus and its response form a thermodynamic cycle of perception that can be modeled by the Carnot engine. The endothermic reversed Carnot engine relies on energy from the environment to increase entropy (i.e., the synaptic complexity of the resting state). High entropy relies on mental energy, which represents intrinsic motivation and focuses on the future. It increases freedom of action. The Carnot engine can model exothermic, negative emotional states, which direct the focus on the past. The organism dumps entropy and energy to its environment, in the form of aggravation, anxiety, criticism, and physical violence. The loss of mental energy curtails freedom of action, forming apathy, depression, mental diseases, and immune problems. Our improving intuition about the brain's intelligent computations will allow the development of new treatments for mental disease and novel find applications in robotics and artificial intelligence.

Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry

INTRODUCTION

Machine learning capability holds promise to inform disease models, the discovery and development of novel disease modifying therapeutics and prevention strategies in psychiatry. Herein, we provide an introduction on how machine learning/Artificial Intelligence (AI) may instantiate such capabilities, as well as provide rationale for its application to psychiatry in both research and clinical ecosystems.

METHODS

Databases PubMed and PsycINFO were searched from 1966 to June 2016 for keywords:Big Data, Machine Learning, Precision Medicine, Artificial Intelligence, Mental Health, Mental Disease, Psychiatry, Data Mining, RDoC, and Research Domain Criteria. Articles selected for review were those that were determined to be aligned with the objective of this particular paper.

RESULTS

Results indicate that AI is a viable option to build useful predictors of outcome while offering objective and comparable accuracy metrics, a unique opportunity, particularly in mental health research. The approach has also consistently brought notable insight into disease models through processing the vast amount of already available multi-domain, semi-structured medical data. The opportunity for AI in psychiatry, in addition to disease-model refinement, is in characterizing those at risk, and it is likely also relevant to personalizing and discovering therapeutics.

CONCLUSIONS

Machine learning currently provides an opportunity to parse disease models in complex, multi-factorial disease states (e.g. mental disorders) and could possibly inform treatment selection with existing therapies and provide bases for domain-based therapeutic discovery.

Increasing physical activity and healthy diet in outpatients with mental disorders: a randomized-controlled evaluation of two psychological interventions

INTRODUCTION

While physical activity (PA) can play an important role in the treatment of mental disorders (MD), large proportions of patients with MD do not meet PA recommendations. The aim of this trial was to evaluate whether structured psychological intervention (MoVo-LISA) is effective in helping outpatients with MD to increase their level of PA. As active control group (CG) we modified MoVo-LISA to target healthy diet behavior.

METHODS

N = 83 outpatients with MD (F1-F4) were randomized to the two conditions. PA (self-report and accelerometry), dietary behavior, social-cognitive determinants of health behavior change, psychiatric symptoms and health-related quality of life were assessed at baseline, 1 and 12 weeks after the intervention.

RESULTS

Significant time*group interaction effects for objectively measured PA, dietary behavior and fruit and vegetable consumption indicated differential effects of the interventions on these outcomes. PA increased in the MoVo-LISA group (IG) from baseline to follow-up while it decreased in CG. IG showed a significant higher level of objectively measured PA at follow-up compared to CG. Dietary behavior and fruit and vegetable consumption significantly increased from baseline to follow-up in CG, but not IG. IG showed a significant increase in some, but not all social cognitive determinants of health behavior change.

CONCLUSIONS

MoVo-LISA is effective in helping outpatients with MD to increase their level of PA in short- and mid-term. The used intervention strategies are effective for the promotion of healthy diet in patients with MD as well.

Short-term PM2.5 exposure and emergency hospital admissions for mental disease

Epidemiologic studies regarding the relationship between short-term exposure to particulate matter with a median aerodynamic diameter < 2.5 µm (PM_2.5) and mental disease are limited. This study examined the effects of short-term exposure to PM_2.5 on emergency admissions to the hospital for mental disease in Seoul, Korea. Data regarding 80,634 emergency admissions for mental diseases were collected from a nationally centralized healthcare claims database in Seoul during 2003-2013. Generalized linear models with climate variables were used to examine associations between short-term PM_2.5 exposure and mental disease admissions. To comprehensively assess PM_2.5 effects, we used single- and two-pollutant models, which considered other pollutants in combination with PM_2.5. The relative risk (RR) of emergency admissions for mental disease was 1.008 (95% confidence interval, 1.001-1.015) for each 10 μg/m³ increase in 2-day average PM_2.5 concentration. This effect persisted or became slightly stronger in the two-pollutant models that included carbon monoxide, nitrogen dioxide, ozone, or sulfur dioxide (RR, 1.01-1.021), but association appeared to be limited to individuals < 65 years of age. Significant association was estimated only during the warm season (RR, 1.021-1.023) in the two-pollutant models. The exposure-response curve was steeper at lower concentrations, suggesting that the risk of mental disease at lower concentrations of pm2.5 (0-30 µg/m³). PM_2.5 was associated with increased admissions even when it was below the World Health Organization's Air Quality Guidelines (25 μg/m³), but the association was not statistically significant. Thus, based on the data from a large database, exposure to PM_2.5 was associated with increases in emergency admissions for mental diseases, and this association was significant during the warm season. PM_2.5 may even affect mental disease at levels below the current air quality guidelines. These results provide substantial insight regarding the effects of air pollutants and have important implications for policy makers.

Teenage Trauma Patients Are at Increased Risk for Readmission for Mental Diseases and Disorders

BACKGROUND

Most studies of readmission after trauma are limited to single institutions or single states. The purpose of this study was to determine the risk factors for readmission after trauma for mental illness including readmissions to different hospitals across the United States.

MATERIALS AND METHODS

The Nationwide Readmission Database for 2013 and 2014 was queried for all patients aged 13 to 64 y with a nonelective admission for trauma and a nonelective readmission within 30 d. Multivariable logistic regression was performed for readmission for mental diseases and disorders.

RESULTS

During the study period, 53,402 patients were readmitted within 30 d after trauma. The most common major diagnostic category on readmission was mental diseases and disorders (12.1%). The age group with the highest percentage of readmissions for mental diseases and disorders was 13 to 17 y (38%). On multivariable regression, the teenage group was also the most likely to be readmitted for mental diseases and disorders compared to 18-44 y (odds ratio [OR] 0.45, P < 0.01) and 45-64 y (OR 0.24, P < 0.01). Other high-risk comorbidities included HIV infection (OR 2.4, P < 0.01), psychosis (OR 2.2, P < 0.01), drug (OR 2.0, P < 0.01), and alcohol (OR 1.4, P < 0.01) abuse.

CONCLUSIONS

Teenage trauma patients are at increased risk for hospital readmission for mental illness. Efforts to reduce these admissions should be targeted toward individuals with high-risk comorbidities such as HIV infection, psychosis, and substance abuse.

Factors associated with the burden of family caregivers of patients with mental disorders: a cross-sectional study

BACKGROUND

Caregivers are responsible for the home care of family members with mental-health disorders often experience changes in their life that can generate stress and burden. The aim of this study was to identify factors associated with the burden of caregivers of family members with mental disorders.

METHODS

This cross-sectional study was conducted with a non-probability sample of family caregivers, whose patients attended a community services program, the Psychosocial Care Centers, in three cities in the southwest region of Goiás State, Central Brazil. Data collection took place from June 2014 to June 2015. The participants were 281 caregivers who completed a sociodemographic questionnaire and the Zarit Burden Interview (ZBI). Bivariate analyses (t test, analysis of variance, and Pearson correlation) were performed, and variables with values of p < 0.10 and gender were included in a multiple-linear regression model. Values of p < 0.05 were considered significant.

RESULTS

The caregivers were mostly female and parents of the patients, were married, with low education, and of low income. The mean ZBI score was 27.66. The factors independently associated with caregivers' burden were depression, being over 60 years of age, receiving no help with caregiving, recent patient crisis, contact days, and having other family members needing care.

CONCLUSIONS

This study identified factors that deserve the attention of community services and can guide programs, such as family psycho-education groups, which may help to minimize or prevent the effects of burden on family caregivers responsible for patients' home care.