Reduced insulin sensitivity may be related to less striatal glutamate: An 1H-MRS study in healthy non-obese humans

Meta-analysis and open-source database for in vivo brain Magnetic Resonance spectroscopy in health and disease

Proton (1H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo. Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.

Meta-analysis and Open-source Database for In Vivo Brain Magnetic Resonance Spectroscopy in Health and Disease

Proton ( 1 H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo . Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T 2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.

Investigating the effects of antipsychotics on brain insulin action: Study protocol for a multi-modality magnetic resonance imaging (MRI) study in healthy controls

Antipsychotics (APs) are the cornerstone of treatment for schizophrenia (SCZ) but are unfortunately associated with serious metabolic adverse effects including weight gain and type 2 diabetes. The pathophysiology of AP-induced metabolic dysfunction is largely undetermined. Brain insulin resistance has been posited to be at the cross-roads of many cognitive and metabolic disorders, and disruption of central insulin action has emerged as a possible explanatory mechanism underlying AP induced metabolic dysfunction. Previous studies suggest that change in neuroimaging-based parameters with intranasal insulin administration can be leveraged to investigate brain insulin resistance. In this proof-of-concept study, we will utilize neural signatures of insulin action in the brain to examine if APs disrupt brain insulin signaling. It is hypothesized that: 1) intranasal insulin (INI), but not intranasal placebo (INP), will change cerebral blood flow and resting state connectivity, as well as increase glutamate levels in the striatum and dorsolateral prefrontal cortex; 2) oral olanzapine (OLA), but not oral placebo (PL), will inhibit the effect of INI on these parameters. Thirty-two healthy volunteers will undergo a single blind, cross-over design, wherein all participants receive the following four treatment combinations, 2-6 weeks apart, in a random sequence: INP + PL, INP + OLA, INI + PL, and INI + OLA. Participants will undergo an MRI-based assay of brain insulin resistance 15 minutes after administering 160 IU INI or INP. The scanning protocol includes resting and task-based functional MRI, arterial spin labelling, and proton magnetic resonance spectroscopy. Demonstrating that OLA can acutely induce brain insulin resistance is clinically relevant to metabolic health in SCZ. Evidence of brain insulin resistance induced by acute AP dosing can inform the early use of adjunctive insulin sensitizers for the treatment of metabolic comorbidities associated with AP treatment in severe mental illness. Trial registration ClinicalTrials.gov Registration: NCT03741478.

A narrative review of highly processed food addiction across the lifespan

Evidence is growing that highly processed (HP) foods (i.e., foods high in refined carbohydrates and fat) are highly effective in activating reward systems and may even be capable of triggering addictive processes. Unlike traditional drugs of abuse, exposure to HP foods is common very early in development. HP food addiction has been associated with negative outcomes, including higher body mass index (BMI), more frequent binge eating, greater failure in weight loss treatment trials, and poorer mental and physical health. Although most research on HP food addiction has been conducted using adult samples, research on this topic now spans across the life span beginning in utero and extending through older adulthood. HP food addiction and related reward-based changes are associated with negative outcomes at every life stage, which has important implications for developmentally tailored prevention and treatment efforts. Using a developmentally informed approach, the current study comprehensively reviews the existing research on HP food addiction across the lifespan and highlights important areas of future research.

A chemical-specific adjustment factor for human interindividual differences in kinetics for glutamates (E620-625)

Use of a default methodology for establishment of a health-based guidance value (HBGV) resulted in a group acceptable daily intake (ADI) for glutamates (E620-625) below the normal dietary glutamate intake, and also lower than the intake of free glutamate by breast fed babies. Use of a chemical-specific adjustment factor (CSAF) may overcome this problem. The present study investigates the interindividual human variability in glutamate plasma and brain levels in order to define a CSAF for the interindividual variation in kinetics, a HK_AF, for glutamates. Human clinical data on plasma glutamate levels available from different groups of subjects at Mitsui Memorial Hospital as well as literature data on plasma and brain-related glutamate levels were collected and analysed. The median HK_AF value obtained amounted to 2.62-2.74 to 2.33-2.52 for plasma derived values and to 1.68-1.81 for brain derived values. Combining these values with the CSAF for the interspecies differences in kinetics of 1 and the default factors for interspecies and interindividual differences in dynamics of 2.5 and 3.16 results in an overall CSAF of 16-20. Using this CSAF will result in a HBGV for glutamate that is no longer below the acceptable range of oral intake (AROI).

Brain insulin action in schizophrenia: Something borrowed and something new

Insulin signaling in the central nervous system is at the intersection of brain and body interactions, and represents a fundamental link between metabolic and cognitive disorders. Abnormalities in brain insulin action could underlie the development of comorbid schizophrenia and type 2 diabetes. Among its functions, central nervous system insulin is involved in regulation of striatal dopamine levels, peripheral glucose homeostasis, and feeding regulation. In this review, we discuss the role and importance of central nervous system insulin in schizophrenia and diabetes pathogenesis from a historical and mechanistic perspective. We describe central nervous system insulin sites and pathways of action, with special emphasis on glucose metabolism, cognitive functioning, inflammation, and food preferences. Finally, we suggest possible mechanisms that may explain the actions of central nervous system insulin in relation to schizophrenia and diabetes, focusing on glutamate and dopamine signaling, intracellular signal transduction pathways, and brain energetics. Understanding the interplay between central nervous system insulin and schizophrenia is essential to disentangling this comorbid relationship and may provide novel treatment approaches for both neuropsychiatric and metabolic dysfunction. This article is part of the issue entitled 'Special Issue on Antipsychotics'.