Obesity risk is associated with altered cerebral glucose metabolism and decreased μ-opioid and CB1 receptor availability

BackgroundObesity is a pressing public health concern worldwide. Novel pharmacological means are urgently needed to combat the increase of obesity and accompanying type 2 diabetes (T2D). Although fully established obesity is associated with neuromolecular alterations and insulin resistance in the brain, potential obesity-promoting mechanisms in the central nervous system have remained elusive. In this triple-tracer positron emission tomography study, we investigated whether brain insulin signaling, μ-opioid receptors (MORs) and cannabinoid CB1 receptors (CB1Rs) are associated with risk for developing obesity.MethodsSubjects were 41 young non-obese males with variable obesity risk profiles. Obesity risk was assessed by subjects’ physical exercise habits, body mass index and familial risk factors, including parental obesity and T2D. Brain glucose uptake was quantified with [18F]FDG during hyperinsulinemic euglycemic clamp, MORs were quantified with [11C]carfentanil and CB1Rs with [18F]FMPEP-d2.ResultsSubjects with higher obesity risk had globally increased insulin-stimulated brain glucose uptake (19 high-risk subjects versus 19 low-risk subjects), and familial obesity risk factors were associated with increased brain glucose uptake (38 subjects) but decreased availability of MORs (41 subjects) and CB1Rs (36 subjects).ConclusionsThese results suggest that the hereditary mechanisms promoting obesity may be partly mediated via insulin, opioid and endocannabinoid messaging systems in the brain.</p

Comparative effects of dexmedetomidine, propofol, sevoflurane, and S-ketamine on regional cerebral glucose metabolism in humans: a positron emission tomography study

Introduction: The highly selective alpha(2)-agonist dexmedetomidine has become a popular sedative for neurointensive care patients. However, earlier studies have raised concern that dexmedetomidine might reduce cerebral blood flow without a concomitant decrease in metabolism. Here, we compared the effects of dexmedetomidine on the regional cerebral metabolic rate of glucose (CMRglu) with three commonly used anaesthetic drugs at equi-sedative doses.Methods: One hundred and sixty healthy male subjects were randomised to EC50 for verbal command of dexmedetomidine (1.5 ng ml (1); n=40), propofol (1.7 mu g ml (1); n=40), sevoflurane (0.9% end-tidal; n=40) or S-ketamine (0.75 mu g ml (1); n=20) or placebo (n=20). Anaesthetics were administered using target-controlled infusion or vapouriser with end-tidal monitoring. F-18-labelled fluorodeoxyglucose was administered 20 min after commencement of anaesthetic administration, and high-resolution positron emission tomography with arterial blood activity samples was used to quantify absolute CMRglu for whole brain and 15 brain regions.Results: At the time of [F-18]fluorodeoxyglucose injection, 55% of dexmedetomidine, 45% of propofol, 85% of sevoflurane, 45% of S-ketamine, and 0% of placebo subjects were unresponsive. Whole brain CMRglu was 63%, 71%, 71%, and 96% of placebo in the dexmedetomidine, propofol, sevoflurane, and S-ketamine groups, respectively (PConclusions: At equi-sedative doses in humans, potency in reducing CMRglu was dexmedetomidine>propofol>ketamine=placebo. These findings alleviate concerns for dexmedetomidine-induced vasoconstriction and cerebral ischaemia.</p

Rupture of Abdominal Aortic Aneurysms in Patients Under Screening Age and Elective Repair Threshold

Peer reviewe

Optimized Mapping of Gas-Phase Quantum Calculations to General Force Field Lennard-Jones Parameters Based on Liquid-State Data

We utilize a previously described Minimal Basis Iterative Stockholder (MBIS) method to carry out an atoms-in-molecules partitioning of electron densities. Information from these atomic densities is then mapped to Lennard-Jones parameters using a set of mapping parameters much smaller than the typical number of atom types in a force field. This approach is advantageous in two ways: it eliminates atom types by allowing each atom to have unique Lennard-Jones parameters, and it greatly reduces the number of parameters to be optimized. We show that this approach yields results comparable to those obtained with the typed GAFF force field, even when trained on a relatively small amount of experimental data

Data-Driven Mapping of Gas-Phase Quantum Calculations to General Force Field Lennard-Jones Parameters.

Molecular dynamics simulations are helpful tools for a range of applications, ranging from drug discovery to protein structure determination. The successful use of this technology largely depends on the potential function, or force field, used to determine the potential energy at each configuration of the system. Most force fields encode all of the relevant parameters to be used in distinct atom types, each associated with parameters for all parts of the force field, typically bond stretches, angle bends, torsions, and nonbonded terms accounting for van der Waals and electrostatic interactions. Much attention has been paid to the nonbonded parameters and their derivation, which are important in particular due to their governance of noncovalent interactions, such as protein-ligand binding. Parametrization involves adjusting the nonbonded parameters to minimize the error between simulation results and experimental properties, such as heats of vaporization and densities of neat liquids. In this setting, determining the best set of atom types is far from trivial, and the large number of parameters to be fit for the atom types in a typical force field can make it difficult to approach a true optimum. Here, we utilize a previously described Minimal Basis Iterative Stockholder (MBIS) method to carry out an atoms-in-molecules partitioning of electron densities. Information from these atomic densities is then mapped to Lennard-Jones parameters using a set of mapping parameters much smaller than the typical number of atom types in a force field. This approach is advantageous in two ways: it eliminates atom types by allowing each atom to have unique Lennard-Jones parameters, and it greatly reduces the number of parameters to be optimized. We show that this approach yields results comparable to those obtained with the typed GAFF 1.7 force field, even when trained on a relatively small amount of experimental data