Schizophrenia: a disorder of broken brain bioenergetics.

A substantial and diverse body of literature suggests that the pathophysiology of schizophrenia is related to deficits of bioenergetic function. While antipsychotics are an effective therapy for the management of positive psychotic symptoms, they are not efficacious for the complete schizophrenia symptom profile, such as the negative and cognitive symptoms. In this review, we discuss the relationship between dysfunction of various metabolic pathways across different brain regions in relation to schizophrenia. We contend that several bioenergetic subprocesses are affected across the brain and such deficits are a core feature of the illness. We provide an overview of central perturbations of insulin signaling, glycolysis, pentose-phosphate pathway, tricarboxylic acid cycle, and oxidative phosphorylation in schizophrenia. Importantly, we discuss pharmacologic and nonpharmacologic interventions that target these pathways and how such interventions may be exploited to improve the symptoms of schizophrenia.

Ensemble machine learning model trained on a new synthesized dataset generalizes well for stress prediction using wearable devices.

INTRODUCTION: Advances in wearable sensor technology have enabled the collection of biomarkers that may correlate with levels of elevated stress. While significant research has been done in this domain, specifically in using machine learning to detect elevated levels of stress, the challenge of producing a machine learning model capable of generalizing well for use on new, unseen data remain. Acute stress response has both subjective, psychological and objectively measurable, biological components that can be expressed differently from person to person, further complicating the development of a generic stress measurement model. Another challenge is the lack of large, publicly available datasets labeled for stress response that can be used to develop robust machine learning models. In this paper, we first investigate the generalization ability of models built on datasets containing a small number of subjects, recorded in single study protocols. Next, we propose and evaluate methods combining these datasets into a single, large dataset to study the generalization capability of machine learning models built on larger datasets. Finally, we propose and evaluate the use of ensemble techniques by combining gradient boosting with an artificial neural network to measure predictive power on new, unseen data. In favor of reproducible research and to assist the community advance the field, we make all our experimental data and code publicly available through Github at https://github.com/xalentis/Stress. This paper's in-depth study of machine learning model generalization for stress detection provides an important foundation for the further study of stress response measurement using sensor biomarkers, recorded with wearable technologies. METHODS: Sensor biomarker data from six public datasets were utilized in this study. Exploratory data analysis was performed to understand the physiological variance between study subjects, and the complexity it introduces in building machine learning models capable of detecting elevated levels of stress on new, unseen data. To test model generalization, we developed a gradient boosting model trained on one dataset (SWELL), and tested its predictive power on two datasets previously used in other studies (WESAD, NEURO). Next, we merged four small datasets, i.e. (SWELL, NEURO, WESAD, UBFC-Phys), to provide a combined total of 99 subjects, and applied feature engineering to generate additional features utilizing statistical summaries, with sliding windows of 25 s. We name this large dataset, StressData. In addition, we utilized random sampling on StressData combined with another dataset (EXAM) to build a larger training dataset consisting of 200 synthesized subjects, which we name SynthesizedStressData. Finally, we developed an ensemble model that combines our gradient boosting model with an artificial neural network, and tested it using Leave-One-Subject-Out (LOSO) validation, and on two additional, unseen publicly available stress biomarker datasets (WESAD and Toadstool). RESULTS: Our results show that previous models built on datasets containing a small number (<50) of subjects, recorded in single study protocols, cannot generalize well to new, unseen datasets. Our presented methodology for generating a large, synthesized training dataset by utilizing random sampling to construct scenarios closely aligned with experimental conditions demonstrate significant benefits. When combined with feature-engineering and ensemble learning, our method delivers a robust stress measurement system capable of achieving 85% predictive accuracy on new, unseen validation data, achieving a 25% performance improvement over single models trained on small datasets. The resulting model can be used as both a classification or regression predictor for estimating the level of perceived stress, when applied on specific sensor biomarkers recorded using a wearable device, while further allowing researchers to construct large, varied datasets for training machine learning models that closely emulate their exact experimental conditions. CONCLUSION: Models trained on small, single study protocol datasets do not generalize well for use on new, unseen data and lack statistical power. Machine learning models trained on a dataset containing a larger number of varied study subjects capture physiological variance better, resulting in more robust stress detection. Feature-engineering assists in capturing these physiological variance, and this is further improved by utilizing ensemble techniques by combining the predictive power of different machine learning models, each capable of learning unique signals contained within the data. While there is a general lack of large, labeled public datasets that can be utilized for training machine learning models capable of accurately measuring levels of acute stress, random sampling techniques can successfully be applied to construct larger, varied datasets from these smaller sample datasets, for building robust machine learning models.

Cross-sectional association of seafood consumption, polyunsaturated fatty acids and depressive symptoms in two Torres Strait communities.

Background Dietary intake of long-chain omega 3 (n-3) polyunsaturated fatty acids (LCPUFA) represents a putative modifiable risk factor for depression, and a high ratio of omega 6 (n-6) to n-3 LCPUFA is frequently observed in patients with major depressive disorder. Recent reports suggest that the availability of fish and seafood may be associated with lower depression rates. The aim of this study was to investigate associations of fish consumption and LCPUFA levels with depressive symptoms.Methods Participants for this cross-sectional study (n=206) were recruited at a community screening programme in two Torres Strait Islander communities (Mer and Waiben). Depressive symptoms were assessed with the adapted Patient Health Questionnaire-9 (aPHQ-9) and diet with a structured questionnaire. LCPUFA concentrations were measured with a capillary dried blood spot system (PUFAcoat). Logistic and quantile regression modelling was used to test the relationship between seafood consumption, membrane LCPUFAs and depression scores.Results A higher blood n-6/3 LCPUFA ratio was associated with moderate/severe depression scores across both study sites (OR=1.59 (95%CI 1.09-2.34), P = .017). Seafood consumption was higher and the proportion of participants with aPHQ-9 scores above the cut-off for depression was lower on Mer (n = 100) compared with Waiben (n = 106). Higher seafood consumption was associated with lower depression scores on Waiben (B = -0.57 (95%CI -0.98 - -0.16), P = .006) but not on Mer.Conclusions Our findings support an association of n-3 LCPUFA from natural sources with depressive symptoms. The availability of fresh seafood in the local diet may represent a protective factor for depression in this setting.

Hair cortisol, allostatic load, and depressive symptoms in Australian Aboriginal and Torres Strait Islander people.

Chronic stress and adversity are associated with poor mental health and are thought to contribute to the existing mental health gap between Aboriginal and Torres Strait Islander people and other Australians. Hair cortisol and allostatic load (AL) are indices of sustained stress and may be mediators of the effects of stress on health. The aim of this study was to examine the relationship between hair cortisol, AL, and depressive symptoms. This cross-sectional study comprised 329 Aboriginal and Torres Strait Islander adolescents and adults recruited at two health screening programs operating in three communities in north Queensland. We measured hair cortisol and calculated an AL index from 10 biomarkers. We assessed depressive symptoms with a version of the Patient Health Questionnaire-9 adapted for Aboriginal and Torres Strait Islander people (aPHQ-9). We found differences in cortisol and AL between the screening programs and communities, which were not explained by depressive symptoms. Overall aPHQ-9 scores were unrelated to hair cortisol (p = .25 and p = .94) and AL (p = .30 and p = .88) when age, gender and smoking were taken into account. However, anhedonia (p = .007) and insomnia (p = .006) sub-scores were each significantly associated with AL in one study site. Our present data did not demonstrate overall associations of stress biomarkers and multisystem dysregulation with depressive symptoms, which suggests that the relationship between cumulative stress and depression may be better explained by other factors in this population. The specific association between anhedonia and insomnia with AL indicates that chronic multisystem dysregulation plays a role in these features of depression in this population. Lay summary Our study investigated the relationship between symptoms of depression and two biological pathways thought to mediate depression risk - the stress hormone cortisol and allostatic load (AL) - in an Australian Aboriginal and Torres Strait Islander population. Overall, cortisol and AL were unrelated to depression. However, AL was selectively associated with anhedonia (lack of motivation or drive) and sleep disturbances. These results suggest that metabolic dysregulation measured as AL may be relevant to the depression risk in this population.

Glucose homeostasis in major depression and schizophrenia: a comparison among drug-naïve first-episode patients.

There is evidence for insulin resistance in drug-naïve first-episode schizophrenia (Sz) patients. We have tested whether impaired insulin homeostasis is also present in first-episode patients with major depression (MD) and if this can be discerned from stress-related and medication effects. Homeostatic model assessment of insulin resistance (HOMA-IR) was determined in a cross-sectional cohort study of acute first-episode drug-naïve patients with MD (n = 18) or Sz (n = 24), and healthy controls (C, n = 43). Morning cortisol and catecholamine metabolites were assessed to control for hormonal stress axis activation. Subjects were matched for sex, age, body mass index and waist-hip ratio to exclude the possibility that overweight and visceral adiposity were potential confounding factors. HOMA-IR did not differ between MD and controls, but was increased in Sz compared to MD (p = 0.002) and controls (p = 0.012). Catecholamine metabolites were elevated in both patient groups, indicating presence of hormonal stress axis activation. However, diagnosis-related changes of HOMA-IR were independent from this. Impaired insulin sensitivity was absent in MD, but specifically related to the early disease course of Sz. Thus, considering previous studies in this field, MD may be related to impaired glucose/insulin homeostasis in the long-term but not in early disease stages.

Social rank does not affect sperm quality in male African wild dogs (Lycaon pictus).

Sperm banking and AI could benefit endangered African wild dog conservation. However, it is unclear whether their dominance hierarchy causes a decrease in reproductive and sperm quality parameters in subordinate males that typically do not breed. In this study, we investigated the effect of social rank on male reproductive parameters, including faecal androgen and glucocorticoid metabolite concentrations, prostate and testes volume, preputial gland size, semen collection success and sperm quality. Samples were obtained from captive males (prebreeding season: n=12 from four packs; breeding season: n=24 from seven packs) that were classified as alpha (dominant), beta or gamma (subordinates) based on the frequency of dominant versus submissive behaviours. In the prebreeding season, semen was successfully collected from all alpha but only half the subordinate males, with urine contamination (associated with lower rank) significantly reducing total and progressive motility, sperm motility index, normal sperm morphology and acrosome integrity. The breeding season was associated with a significant increase in faecal androgens, prostate and testis volume, as well as progressive motility and the total number of spermatozoa ejaculated. However, with the exception of prostate volume (mean±s.e.m: 12.5±4.5, 7.1±1.0 and 7.3±1.0cm3 in alpha, beta and gamma males respectively; P=0.035), all other reproductive and sperm quality parameters did not differ between males of each social rank. In conclusion, reproductive suppression of subordinate males appears to be behaviourally mediated, because males of all social ranks produce semen of similar quality, making them suitable candidates for sperm banking, particularly during the breeding season when sperm quality improves.

The Therapeutic Potential of Ketogenic Diet Throughout Life: Focus on Metabolic, Neurodevelopmental and Neurodegenerative Disorders.

This chapter reviews the efficacy of the ketogenic diet in a variety of neurodegenerative, neurodevelopmental and metabolic conditions throughout different stages of life. It describes conditions affecting children, metabolic disorders in adults and disorderrs affecting the elderly. We have focused on application of the ketogenic diet in clinical studies and in preclinical models and discuss the benefits and negative aspects of the diet. Finally, we highlight the need for further research in this area with a view of discovering novel mechanistic targets of the ketogenic diet, as a means of maximising the potential benefits/risks ratio.

Chronic Helminth Infection Perturbs the Gut-Brain Axis, Promotes Neuropathology, and Alters Behavior.

Helminth infections in children are associated with impaired cognitive development; however, the biological mechanisms for this remain unclear. Using a murine model of gastrointestinal helminth infection, we demonstrate that early-life exposure to helminths promotes local and systemic inflammatory responses and transient changes in the gastrointestinal microbiome. Behavioral and cognitive analyses performed 9-months postinfection revealed deficits in spatial recognition memory and an anxiety-like behavioral phenotype in worm-infected mice, which was associated with neuropathology and increased microglial activation within the brain. This study demonstrates a previously unrecognized mechanism through which helminth infections may influence cognitive function, via perturbations in the gut-immune-brain axis.

Connecting Brain Proteomics with Behavioural Neuroscience in Translational Animal Models of Neuropsychiatric Disorders.

Modelling psychiatric disorders in animals has been hindered by several challenges related to our poor understanding of the disease causes. This chapter describes recent advances in translational research which may lead to animal models and relevant proteomic biomarkers that can be informative about disease mechanisms and potential new therapeutic targets. The review focuses on the behavioural and molecular correlates in models of schizophrenia and major depressive disorder, as guided by recently established Research Domain Criteria (RDoC). This approach is based on providing proteomic data for aetiologically driven, behaviourally well-characterised animal models to link discovered biomarker candidates with the human disease.

Allostatic load mediates the impact of stress and trauma on physical and mental health in Indigenous Australians.

OBJECTIVES: A considerable gap exists in health and social emotional well-being between Indigenous people and non-Indigenous Australians. Recent research in stress neurobiology highlights biological pathways that link early adversity and traumas as well as life stresses to ill health. We argue that the neurobiological stress response and its maladaptive changes, termed allostatic load, provide a useful framework to understand how adversity leads to physical and mental illness in Indigenous people. In this paper we review the biology of allostatic load and make links between stress-induced systemic hormonal, metabolic and immunological changes and physical and mental illnesses. CONCLUSIONS: Exposure to chronic stress throughout life results in an increased allostatic load that may contribute to a number of metabolic, cardiovascular and mental disorders that shorten life expectancy in Indigenous Australians.

"More than skin deep": stress neurobiology and mental health consequences of racial discrimination.

Ethnic minority groups across the world face a complex set of adverse social and psychological challenges linked to their minority status, often involving racial discrimination. Racial discrimination is increasingly recognized as an important contributing factor to health disparities among non-dominant ethnic minorities. A growing body of literature has recognized these health disparities and has investigated the relationship between racial discrimination and poor health outcomes. Chronically elevated cortisol levels and a dysregulated hypothalamic-pituitary-adrenal (HPA) axis appear to mediate effects of racial discrimination on allostatic load and disease. Racial discrimination seems to converge on the anterior cingulate cortex (ACC) and may impair the function of the prefrontal cortex (PFC), hence showing substantial similarities to chronic social stress. This review provides a summary of recent literature on hormonal and neural effects of racial discrimination and a synthesis of potential neurobiological pathways by which discrimination affects mental health.

Mental health consequences of stress and trauma: allostatic load markers for practice and policy with a focus on Indigenous health.

OBJECTIVE: Mental health, well-being, and social life are intimately related as is evident from the higher incidence of psychiatric illness in individuals exposed to social stress and adversity. Several biological pathways linking social adversity to health outcomes are heavily investigated in the aims of facilitating early identification and prevention of adverse health outcomes. We provide a practice-orientated overview of the allostatic load model and how it relates to metabolic and cardiovascular comorbidity in psychiatric disorders. CONCLUSIONS: Allostatic load brings together a set of neuroendocrine, metabolic, immune and cardiovascular biomarkers that are elevated in individuals with adverse early life experiences and are predictive of cardiovascular and metabolic risk in psychiatric illness of critical importance for Indigenous Australians.

Generalizable machine learning for stress monitoring from wearable devices: A systematic literature review.

INTRODUCTION: Wearable sensors have shown promise as a non-intrusive method for collecting biomarkers that may correlate with levels of elevated stress. Stressors cause a variety of biological responses, and these physiological reactions can be measured using biomarkers including Heart Rate Variability (HRV), Electrodermal Activity (EDA) and Heart Rate (HR) that represent the stress response from the Hypothalamic-Pituitary-Adrenal (HPA) axis, the Autonomic Nervous System (ANS), and the immune system. While Cortisol response magnitude remains the gold standard indicator for stress assessment [1], recent advances in wearable technologies have resulted in the availability of a number of consumer devices capable of recording HRV, EDA and HR sensor biomarkers, amongst other signals. At the same time, researchers have been applying machine learning techniques to the recorded biomarkers in order to build models that may be able to predict elevated levels of stress. OBJECTIVE: The aim of this review is to provide an overview of machine learning techniques utilized in prior research with a specific focus on model generalization when using these public datasets as training data. We also shed light on the challenges and opportunities that machine learning-enabled stress monitoring and detection face. METHODS: This study reviewed published works contributing and/or using public datasets designed for detecting stress and their associated machine learning methods. The electronic databases of Google Scholar, Crossref, DOAJ and PubMed were searched for relevant articles and a total of 33 articles were identified and included in the final analysis. The reviewed works were synthesized into three categories of publicly available stress datasets, machine learning techniques applied using those, and future research directions. For the machine learning studies reviewed, we provide an analysis of their approach to results validation and model generalization. The quality assessment of the included studies was conducted in accordance with the IJMEDI checklist [2]. RESULTS: A number of public datasets were identified that are labeled for stress detection. These datasets were most commonly produced from sensor biomarker data recorded using the Empatica E4 device, a well-studied, medical-grade wrist-worn wearable that provides sensor biomarkers most notable to correlate with elevated levels of stress. Most of the reviewed datasets contain less than twenty-four hours of data, and the varied experimental conditions and labeling methodologies potentially limit their ability to generalize for unseen data. In addition, we discuss that previous works show shortcomings in areas such as their labeling protocols, lack of statistical power, validity of stress biomarkers, and model generalization ability. CONCLUSION: Health tracking and monitoring using wearable devices is growing in popularity, while the generalization of existing machine learning models still requires further study, and research in this area will continue to provide improvements as newer and more substantial datasets become available.

The role of combined modifiable lifestyle behaviors in the longitudinal association between stressful life events and allostatic load in Australian adults.

Allostatic load is a model that is used to quantify the physiological damage from exposure to stressors. Stressful life events are chronic stressors that can lead to an elevated allostatic load through the physiological and behavioral stress responses. However, there is limited empirical studies that has tested the proposed behavioural pathway. Our study addresses this gap by examining the mediating role of combined modifiable lifestyle behaviors in the 12-years longitudinal association between stressful life events and allostatic load among participants from the Australian Diabetes, Obesity and Lifestyle (AusDiab) Study cohort. A latent profile analysis was performed to identify latent subgroups with distinct behavioral clusters based on five modifiable lifestyle behaviors (smoking, sedentary behavior, physical activity, alcohol consumption, and diet quality). We then used a sequential mediation model design with path analysis to test the mediating effect of these latent subgroups in the associations between stressful life events and three measures of allostatic load. Indirect effects were estimated using the product of coefficient approach and the statistical significance was determined by the 95% bias-corrected bootstrap confidence intervals with 1000 replications. We identified three latent subgroups: "least healthy lifestyle" (12%; n = 396), "moderately healthy lifestyle" (78.7%; n = 2599), and "most healthy lifestyle" (9.2%; n = 306). Exposure to stressful life events was not associated with the allocation of participants in latent subgroups. Compared to the "moderately healthy lifestyle" subgroups, we found that the "least healthy lifestyle" behavioral cluster was not associated with allostatic load. However, there was a significant inverse association between the "most healthy lifestyle" behavioral cluster and allostatic load. Overall, we did not find significant indirect effects between stressful life events and three measures of allostatic load via the "least healthy lifestyle" and the "most healthy lifestyle" groups. In summary, the combinations of modifiable lifestyle behaviors did not explain the association between stressful life events and allostatic load. More longitudinal studies are needed to replicate our study to confirm this finding.

Molecular validation of the acute phencyclidine rat model for schizophrenia: identification of translational changes in energy metabolism and neurotransmission.

Administration of the noncompetitive N-methyl-D-aspartate (NMDA) receptor antagonist phencyclidine (PCP) to rodents is widely used as preclinical model for schizophrenia. Most studies on this model employ methods investigating behavior and brain abnormalities. However, little is known about the corresponding peripheral effects. In this study, we analyzed changes in brain and serum molecular profiles, together with alterations in behavior after acute PCP treatment of rats. Furthermore, abnormalities in peripheral protein expression of first and recent onset antipsychotic free schizophrenia patients were assessed for comparison with the preclinical model. PCP treatment induced hyperlocomotion and stereotypic behavior, which have been related to positive symptoms of schizophrenia. Multiplex immunoassay profiling of serum revealed molecular abnormalities similar to those seen in first and recent onset, antipsychotic free schizophrenia patients. Also, increased insulin levels were detected after administration of a glucose tolerance test (GTT), consistent with previous studies showing changes in insulin signaling in patients with schizophrenia. Finally, schizophrenia-relevant alterations in brain molecules were found in the hippocampus and to a lesser extent in the frontal cortex using liquid-chromatography mass spectrometry and (1)H nuclear magnetic resonance spectroscopy. In conclusion, this study identified behavioral and molecular alterations in the acute PCP rat model, which are also observed in human schizophrenia. We propose that the corresponding changes in serum in both animals and patients may have utility as surrogate markers in this model to facilitate discovery and development of novel drugs for treatment of certain pathological features of schizophrenia.

Reduced density of hypothalamic VGF-immunoreactive neurons in schizophrenia: a potential link to impaired growth factor signaling and energy homeostasis.

Protein expression of VGF (nonacronymic) is induced by nerve/brain-derived growth factor, neurotrophin 3, and insulin. VGF is synthesized by neurons in the paraventricular (PVN) and supraoptic (SON) nuclei of the hypothalamus. After enzymatic processing, smaller VGF-derived peptides are secreted into the cerebrospinal fluid (CSF) or blood. These peptides play important roles by improving synaptic plasticity, neurogenesis, and energy homeostasis, which are impaired in schizophrenia. Based on previous observations of neuroendocrine and hypothalamic deficits in schizophrenia and to determine whether increased levels of the VGF fragment 23-62 in CSF, which have been described in a recent study, were related to changes in hypothalamic VGF expression, an immunohistochemical study was performed in 20 patients with schizophrenia and 19 matched control subjects. N- (D-20) and C-terminal (R-15) VGF antibodies yielded similar results and immunolabeled a vast majority of PVN and SON neurons. Additionally, D20-VGF immunohistochemistry revealed immunostained fibers in the pituitary stalk and neurohypophysis that ended at vessel walls, suggesting axonal transport and VGF secretion. The cell density of D20-VGF-immunoreactive neurons was reduced in the left PVN (P = 0.002) and SON (P = 0.008) of patients with schizophrenia. This study provides the first evidence for diminished hypothalamic VGF levels in schizophrenia, which might suggest increased protein secretion. Our finding was particularly significant in subjects without metabolic syndrome (patients with a body mass index ≤28.7 kg/m(2)). In conclusion, apart from beneficial effects on synaptic plasticity and neurogenesis, VGF may be linked to schizophrenia-related alterations in energy homeostasis.

Allostatic load and systemic comorbidities in psychiatric disorders.

Psychiatric disorders are complex, disabling, and chronic conditions that are often accompanied by one or more systemic medical comorbidities. In this narrative review, we provide an overview of the allostatic load concept, which represents a multi-system dysregulation in response to chronic stress and link it to systemic comorbidities associated with psychiatric disorders. We synthesized published literature gathered using Medline (Ovid), Scopus, and PsychInfo and identified a high frequency of systemic comorbidities for both mood and psychotic disorders. The identified cardiovascular, metabolic, and immune comorbidities may represent the result of chronic wear and tear caused by a complex interaction between chronic psychosocial stress, health risk behaviors, pharmacological stressors, and the biological systems involved in the development of allostatic load. These findings support the notion that psychiatric disorders should be re-conceptualized as systemic disorders, affecting the brain and systemic biological pathways in an interconnected fashion to result in systemic comorbidities. We suggest that the multi-systemic and multi-dimensional approach that drives the allostatic load concept should be considered for understanding comorbidities in vulnerable psychiatric patients.

Adverse childhood experiences and allostatic load: A systematic review.

Adverse Childhood Experiences (ACEs) are stressful and/or traumatic experiences associated with an increased lifetime risk of negative health outcomes. The Allostatic Load (AL) is a measure of multisystem dysregulation, resulted by chronic stress. We systematically reviewed the English language literature on the association between ACEs and AL to identify the clinical risk profile, with the exclusion of reviews and preclinical studies. Searches covered the publication period up to the 1st of February 2022 and identified 25 studies in which ACEs such as maltreatment, abuse, poverty, psychological abuse, and discrimination were investigated in the context of AL. The selected studies used different sets of AL biomarkers resulting in substantial heterogenicity of calculating the AL index. Overall, we found that ACEs are associated with elevated AL and poorer health outcomes in adulthood. Furthermore, health risk behaviors, social support, and coping resources either moderate or mediate this association. These findings suggest that targeting individuals at risk and starting interventions early might reduce AL and its deleterious health consequences.