Dirk Langemann

Build-ups in the supply chain of the brain: on the neuroenergetic cause of obesity and type 2 diabetes mellitus.

Obesity and type 2 diabetes have become the major health problems in many industrialized countries. A few theoretical frameworks have been set up to derive the possible determinative cause of obesity. One concept views that food availability determines food intake, i.e. that obesity is the result of an external energy “push” into the body. Another one views that the energy milieu within the human organism determines food intake, i.e. that obesity is due to an excessive “pull” from inside the organism. Here we present the unconventional concept that a healthy organism is maintained by a “competent brain-pull” which serves systemic homeostasis, and that the underlying cause of obesity is “incompetent brain-pull”, i.e. that the brain is unable to properly demand glucose from the body. We describe the energy fluxes from the environment, through the body, towards the brain with a mathematical “supply chain” model and test whether its predictions fit medical and experimental data sets from our and other research groups. In this way, we show data-based support of our hypothesis, which states that under conditions of food abundance incompetent brain-pull will lead to build-ups in the supply chain culminating in obesity and type 2 diabetes. In the same way, we demonstrate support of the related hypothesis, which states that under conditions of food deprivation a competent brain-pull mechanism is indispensable for the continuance of the brain´s high energy level. In conclusion, we took the viewpoint of integrative physiology and provided evidence for the necessity of brain-pull mechanisms for the benefit of health. Along these lines, our work supports recent molecular findings from the field of neuroenergetics and continues the work on the “Selfish Brain” theory dealing with the maintenance of the cerebral and peripheral energy homeostasis.

Effect of constitution on mass of individual organs and their association with metabolic rate in humans--a detailed view on allometric scaling.

Resting energy expenditure (REE)-power relationships result from multiple underlying factors including weight and height. In addition, detailed body composition, including fat free mass (FFM) and its components, skeletal muscle mass and internal organs with high metabolic rates (i.e. brain, heart, liver, kidneys), are major determinants of REE. Since the mass of individual organs scales to height as well as to weight (and, thus, to constitution), the variance in these associations may also add to the variance in REE. Here we address body composition (measured by magnetic resonance imaging) and REE (assessed by indirect calorimetry) in a group of 330 healthy volunteers differing with respect to age (17–78 years), sex (61% female) and BMI (15.9–47.8 kg/m2). Using three dimensional data interpolation we found that the inter-individual variance related to scaling of organ mass to height and weight and, thus, the constitution-related variances in either FFM (model 1) or kidneys, muscle, brain and liver (model 2) explained up to 43% of the inter-individual variance in REE. These data are the first evidence that constitution adds to the complexity of REE. Since organs scale differently as weight as well as height the “fit” of organ masses within constitution should be considered as a further trait.

The Selfish Brain: Stress and Eating Behavior

The brain occupies a special hierarchical position in human energy metabolism. If cerebral homeostasis is threatened, the brain behaves in a “selfish” manner by competing for energy resources with the body. Here we present a logistic approach, which is based on the principles of supply and demand known from economics. In this “cerebral supply chain” model, the brain constitutes the final consumer. In order to illustrate the operating mode of the cerebral supply chain, we take experimental data which allow assessing the supply, demand and need of the brain under conditions of psychosocial stress. The experimental results show that the brain under conditions of psychosocial stress actively demands energy from the body, in order to cover its increased energy needs. The data demonstrate that the stressed brain uses a mechanism referred to as “cerebral insulin suppression” to limit glucose fluxes into peripheral tissue (muscle, fat) and to enhance cerebral glucose supply. Furthermore psychosocial stress elicits a marked increase in eating behavior in the post-stress phase. Subjects ingested more carbohydrates without any preference for sweet ingredients. These experimentally observed changes of cerebral demand, supply and need are integrated into a logistic framework describing the supply chain of the selfish brain.

The brain's supply and demand in obesity

During psychosocial stress, the brain demands extra energy from the body to satisfy its increased needs. For that purpose it uses a mechanism referred to as “cerebral insulin suppression” (CIS). Specifically, activation of the stress system suppresses insulin secretion from pancreatic beta-cells, and in this way energy—particularly glucose—is allocated to the brain rather than the periphery. It is unknown, however, how the brain of obese humans organizes its supply and demand during psychosocial stress. To answer this question, we examined 20 obese and 20 normal weight men in two sessions (Trier Social Stress Test and non-stress control condition followed by either a rich buffet or a meager salad). Blood samples were continuously taken and subjects rated their vigilance and mood by standard questionnaires. First, we found a low reactive stress system in obesity. While obese subjects showed a marked hormonal response to the psychosocial challenge, the cortisol response to the subsequent meal was absent. Whereas the brains of normal weight subjects demanded for extra energy from the body by using CIS, CIS was not detectable in obese subjects. Our findings suggest that the absence of CIS in obese subjects is due to the absence of their meal-related cortisol peak. Second, normal weight men were high reactive during psychosocial stress in changing their vigilance, thereby increasing their cerebral energy need, whereas obese men were low reactive in this respect. Third, normal weight subjects preferred carbohydrates after stress to supply their brain, while obese men preferred fat and protein instead. We conclude that the brain of obese people organizes its need, supply, and demand in a low reactive manner.

Why doesn't the brain lose weight, when obese people diet?

Objective: As has been shown recently, obesity is associated with brain volume deficits. We here used an interventional study design to investigate whether the brain shrinks after caloric restriction in obesity. To elucidate mechanisms of neuroprotection we assessed brain-pull competence, i.e. the brain’s ability to properly demand energy from the body. Methods: In 52 normal-weight and 42 obese women (before and after ≈10% weight loss) organ masses of brain, liver and kidneys (magnetic resonance imaging), fat (air displacement plethysmography) and muscle mass (dual-energy X-ray absorptiometry) were assessed. Body metabolism was measured by indirect calorimetry. To investigate how energy is allocated between brain and body, we used reference data obtained in the field of comparative biology. We calculated the distance between each woman and a reference mammal of comparable size in a brain-body plot and named the distance ‘encephalic measure’. To elucidate how the brain protects its mass, we measured fasting insulin, since ‘cerebral insulin suppression’ has been shown to function as a brain-pull mechanism. Results: Brain mass was equal in normal-weight and obese women (1,441.8 ± 14.6 vs. 1,479.2 ± 12.8 g; n.s.) and was unaffected by weight loss (1,483.8 ± 12.7 g; n.s.). In contrast, masses of muscle, fat, liver and kidneys decreased by 3–18% after weight loss (all p < 0.05). The encephalic measure was lower in obese than normal-weight women (5.8 ± 0.1 vs. 7.4 ± 0.1; p < 0.001). Weight loss increased the encephalic measure to 6.3 ± 0.1 (p < 0.001). Insulin concentrations were inversely related to the encephalic measure (r = –0.382; p < 0.001). Conclusion: Brain mass is normal in obese women and is protected during caloric restriction. Our data suggest that neuroprotection during caloric restriction is mediated by a competent brain-pull exerting cerebral insulin suppression.

Multilevel Gauss-Newton methods for phase retrieval problems

The phase retrieval problem is of wide interest because it appears in a number of interesting application areas in physics. Several kinds of phase retrieval problems appeared in laser optics over the past decade. In this paper we consider the numerical solution of two phase retrieval problems for an unknown smooth function f with compact support. We approximate f by a linear spline. The corresponding spline coefficients are iteratively determined by local Gauss–Newton methods, where convenient initial guesses are constructed by a multilevel strategy. We close with some numerical tests which illustrate our method.

The corpulent phenotype—how the brain maximizes survival in stressful environments

The reactivity of the stress system may change during the life course. In many—but not all—humans the stress reactivity decreases, once the individual is chronically exposed to a stressful and unsafe environment (e.g., poverty, work with high demands, unhappy martial relationship). Such an adaptation is referred to as habituation. Stress habituation allows alleviating the burden of chronic stress, particularly cardiovascular morbidity and mortality. Interestingly, two recent experiments demonstrated low stress reactivity during a mental or psychosocial challenge in subjects with a high body mass. In this focused review we attempt to integrate these experimental findings in a larger context. Are these data compatible with data sets showing a prolonged life expectancy in corpulent people? From the perspective of neuroenergetics, we here raise the question whether “obesity” is unhealthy at all. Is the corpulent phenotype possibly the result of “adaptive phenotypic plasticity” allowing optimized survival in stressful environments?

Compact energy metabolism model: Brain controlled energy supply

The regulation of the energy metabolism is crucial to ensure the functionality of the entire organism. Deregulations may lead to severe pathologies such as obesity and type 2 diabetes mellitus. The decisive role of the brain as the active controller and heavy consumer in the complex whole body energy metabolism is the matter of recent research. Latest studies suggest that the brains energy supply has the highest priority while all organs in the organism compete for the available energy resources. In our novel mathematical model, we address these new findings. We integrate energy fluxes and their control signals such as glucose fluxes, insulin signals as well as the ingestion momentum in our new dynamical system. As a novel characteristic, the hormone insulin is regarded as central feedback signal of the brain. Hereby, our model particularly contains the competition for energy between brain and body periphery. The analytical investigation of the presented dynamical system shows a stable long-term behavior of the entire energy metabolism while short time observations demonstrate the typical oscillating blood glucose variations as a consequence of food intake. Our simulation results demonstrate a realistic behavior even in situations like exercise or exhaustion, and key elements like the brains preeminence are reflected. The presented dynamical system is a step towards a systemic understanding of the human energy metabolism and thus may shed light to defects causing diseases based on deregulations in the energy metabolism.

Deductive functional assignment of elements in appetite regulation.

This paper presents a simple mathematical model for energy transport from the body into the brain and for appetite regulation. Particular properties in appetite regulation are deduced from the general observation of cyclic food intake. These particular properties are the importance of a push component, however small it may be, from the body into the brain, the dependence of the appetite activation on the energy supply level in the brain and a necessary condition for the sensitivity of this dependence. The dominant pull component in the energy transport is accompanied by a smaller push component managing this information transport. The properties listed above correspond to physiological observations. For instance, sensitivity is found in the postnatal development of projections between neuropeptide Y (NPY) neurons and pro-opiomelanocortin (POMC) neurons, which release, respectively, the appetite-amplifying and -reducing neuropeptides NPY and α-melanocyte-stimulating hormone at their nerve terminals. The development of these projections determines the change from the permanent feeding of the foetus into the cyclic ingestive behaviour in later life. The correspondence verifies the mathematically-deduced properties, justifies the simple model and supports the technique of the deductive functional assignment.

Phase reconstruction by a multilevel iteratively regularized Gauss–Newton method

In this paper we consider the numerical solution of a phase retrieval problem for a compactly supported, linear spline with the Fourier transform , where values of |f| and at finitely many equispaced nodes are given. The unknown phases of complex spline coefficients fulfil a well-structured system of nonlinear equations. Thus the phase reconstruction leads to a nonlinear inverse problem, which is solved by a multilevel strategy and iterative Tikhonov regularization. The multilevel strategy concentrates the main effort of the solution of the phase retrieval problem in the coarse, less expensive levels and provides convenient initial guesses at the next finer level. On each level, the corresponding nonlinear system is solved by an iteratively regularized Gauss–Newton method. The multilevel strategy is motivated by convergence results of IRGN. This method is applicable to a wide range of examples as shown in several numerical tests for noiseless and noisy data.