Susanne Ditlevsen

The mediation proportion: a structural equation approach for estimating the proportion of exposure effect on outcome explained by an intermediate variable.

It is often of interest to assess how much of the effect of an exposure on a response is mediated through an intermediate variable. However, systematic approaches are lacking, other than assessment of a surrogate marker for the endpoint of a clinical trial. We review a measure of “proportion explained” in the context of observational epidemiologic studies. The measure has been much debated; we show how several of the drawbacks are alleviated when exposures, mediators, and responses are continuous and are embedded in a structural equation framework. These conditions also allow for consideration of several intermediate variables. Binary or categorical variables can be included directly through threshold models. We call this measure the mediation proportion, that is, the part of an exposure effect on outcome explained by a third, intermediate variable. Two examples illustrate the approach. The first example is a randomized clinical trial of the effects of interferon-α on visual acuity in patients with age-related macular degeneration. In this example, the exposure, mediator and response are all binary. The second example is a common problem in social epidemiology—to find the proportion of a social class effect on a health outcome that is mediated by psychologic variables. Both the mediator and the response are composed of several ordered categorical variables, with confounders present. Finally, we extend the example to more than one mediator.

Cynical hostility, socioeconomic position, health behaviors, and symptom load: a cross-sectional analysis in a Danish population-based study.

Objective: To analyze the cross-sectional association between cynical hostility and high symptom load in a Danish population-based study. Furthermore, the aim was to investigate to what extent health risk behaviors mediated this association. Methods: Data were based on a postal questionnaire in a Danish random sample of 3426 men and 3699 women aged 40 or 50 years. Cynical hostility was measured by the 8-item Cynical Distrust Scale. High symptom load was assessed by physiological and mental symptoms experienced within the last 4 weeks. Confounders were age and socioeconomic position, while potential mediators were alcohol consumption, smoking, physical activity, and BMI. Results: Higher cynical hostility was associated with self-reported symptom load. Health behaviors did not seem to mediate this effect. Socioeconomic position was a strong confounder for the effect on both health and health behaviors. After adjustment the effects of hostility on health remained with odds ratios of 2.1 (1.7–2.6) for women and 2.3 (1.8–2.8) for men. Conclusion: After adjustment for socioeconomic position, cynical hostility has an effect on self-reported high symptom load, and this effect is not mediated by health behaviors.

A review of the methods for signal estimation in stochastic diffusion leaky integrate-and-fire neuronal models

Parameters in diffusion neuronal models are divided into two groups; intrinsic and input parameters. Intrinsic parameters are related to the properties of the neuronal membrane and are assumed to be known throughout the paper. Input parameters characterize processes generated outside the neuron and methods for their estimation are reviewed here. Two examples of the diffusion neuronal model, which are based on the integrate-and-fire concept, are investigated—the Ornstein–Uhlenbeck model as the most common one and the Feller model as an illustration of state-dependent behavior in modeling the neuronal input. Two types of experimental data are assumed—intracellular describing the membrane trajectories and extracellular resulting in knowledge of the interspike intervals. The literature on estimation from the trajectories of the diffusion process is extensive and thus the stress in this review is set on the inference made from the interspike intervals.

Mathematical modeling of the glucose-insulin system: a review.

Mathematical modeling of the glucose-insulin feedback system is necessary to the understanding of the homeostatic control, to analyze experimental data, to identify and quantify relevant biophysical parameters, to design clinical trials and to evaluate diabetes prevention or disease modification therapies. Much work has been made over the last 30years, and the time now seems ripe to provide a comprehensive review. The one here proposed is focused on the most important clinical/experimental tests performed to understand the mechanism of glucose homeostasis. The review proceeds from models of pancreatic insulin production, with a coarser/finer level of detail ranging over cellular and subcellular scales, to short-term organ/tissue models accounting for the intra-venous and the oral glucose tolerance tests as well as for the euglycemic hyperinsulinemic clamp, to total-body, long-term diabetes models aiming to represent disease progression in terms of β-cell population dynamics over a long period of years.

Intense synaptic activity enhances temporal resolution in spinal motoneurons.

In neurons, spike timing is determined by integration of synaptic potentials in delicate concert with intrinsic properties. Although the integration time is functionally crucial, it remains elusive during network activity. While mechanisms of rapid processing are well documented in sensory systems, agility in motor systems has received little attention. Here we analyze how intense synaptic activity affects integration time in spinal motoneurons during functional motor activity and report a 10-fold decrease. As a result, action potentials can only be predicted from the membrane potential within 10 ms of their occurrence and detected for less than 10 ms after their occurrence. Being shorter than the average inter-spike interval, the AHP has little effect on integration time and spike timing, which instead is entirely determined by fluctuations in membrane potential caused by the barrage of inhibitory and excitatory synaptic activity. By shortening the effective integration time, this intense synaptic input may serve to facilitate the generation of rapid changes in movements.

Interleukin-18 activates skeletal muscle AMPK and reduces weight gain and insulin resistance in mice

Circulating interleukin (IL)-18 is elevated in obesity, but paradoxically causes hypophagia. We hypothesized that IL-18 may attenuate high-fat diet (HFD)-induced insulin resistance by activating AMP-activated protein kinase (AMPK). We studied mice with a global deletion of the α-isoform of the IL-18 receptor (IL-18R−/−) fed a standard chow or HFD. We next performed gain-of-function experiments in skeletal muscle, in vitro, ex vivo, and in vivo. We show that IL-18 is implicated in metabolic homeostasis, inflammation, and insulin resistance via mechanisms involving the activation of AMPK in skeletal muscle. IL-18R−/− mice display increased weight gain, ectopic lipid deposition, inflammation, and reduced AMPK signaling in skeletal muscle. Treating myotubes or skeletal muscle strips with IL-18 activated AMPK and increased fat oxidation. Moreover, in vivo electroporation of IL-18 into skeletal muscle activated AMPK and concomitantly inhibited HFD-induced weight gain. In summary, IL-18 enhances AMPK signaling and lipid oxidation in skeletal muscle implicating IL-18 in metabolic homeostasis.

Practical estimation of high dimensional stochastic differential mixed-effects models

Stochastic differential equations (SDEs) are established tools for modeling physical phenomena whose dynamics are affected by random noise. By estimating parameters of an SDE, intrinsic randomness of a system around its drift can be identified and separated from the drift itself. When it is of interest to model dynamics within a given population, i.e. to model simultaneously the performance of several experiments or subjects, mixed-effects modelling allows for the distinction of between and within experiment variability. A framework for modeling dynamics within a population using SDEs is proposed, representing simultaneously several sources of variation: variability between experiments using a mixed-effects approach and stochasticity in the individual dynamics, using SDEs. These stochastic differential mixed-effects models have applications in e.g. pharmacokinetics/pharmacodynamics and biomedical modelling. A parameter estimation method is proposed and computational guidelines for an efficient implementation are given. Finally the method is evaluated using simulations from standard models like the two-dimensional Ornstein-Uhlenbeck (OU) and the square root models.

The fast climate fluctuations during the stadial and interstadial climate states

Abstract Rapid climate changes during the last glacial period were first observed in ice-core records (Dansgaard and others, 1982). These shifts between interstadials, called Dansgaard–Oeschger (D-O) events, and stadials or deep glaciation were later seen in Atlantic sediment records (Bond and others, 1993), pointing to the ocean circulation as a strong component in the dynamics of these shifts (Wright and Stocker, 1991). the interstadial states are observed to have a characteristic ``sawtooth’’ shape, indicating a gradual drift of the stable interstadial state toward the stable stadial state. In order to contrast the two climate states, we have separated the δ18O signal from the Greenland Icecore Project ice core into periods corresponding to the two states. the climate variability in the two different climatic states is different (Johnsen and others, 1997). We find that the standard deviation is significantly larger in the stadial than in the interstadial state. Both states are found to have a larger standard deviation than the Holocene part of the record. the correlation times in the different states are difficult to obtain because of limited data resolution and diffusion of the isotopic signal. However, using a statistical technique, we have estimated the correlation times. We do not find significant differences in the correlation times, which are of the order of months, in the different climatic states. These findings are interpreted in the context of a simple linear stochastic model which provides information about the relative roles of the climatic forcing and the stability of the climate state governing the climate variability.

The Morris–Lecar neuron model embeds a leaky integrate-and-fire model

We show that the stochastic Morris–Lecar neuron, in a neighborhood of its stable point, can be approximated by a two-dimensional Ornstein–Uhlenbeck (OU) modulation of a constant circular motion. The associated radial OU process is an example of a leaky integrate-and-fire (LIF) model prior to firing. A new model constructed from a radial OU process together with a simple firing mechanism based on detailed Morris–Lecar firing statistics reproduces the Morris–Lecar Interspike Interval (ISI) distribution, and has the computational advantages of a LIF. The result justifies the large amount of attention paid to the LIF models.

Synaptic inhibition and excitation estimated via the time constant of membrane potential fluctuations

When recording the membrane potential, V, of a neuron it is desirable to be able to extract the synaptic input. Critically, the synaptic input is stochastic and nonreproducible so one is therefore often restricted to single-trial data. Here, we introduce means of estimating the inhibition and excitation and their confidence limits from single sweep trials. The estimates are based on the mean membrane potential, V, and the membrane time constant, τ. The time constant provides the total conductance (G = capacitance/τ) and is extracted from the autocorrelation of V. The synaptic conductances can then be inferred from V when approximating the neuron as a single compartment. We further employ a stochastic model to establish limits of confidence. The method is verified on models and experimental data, where the synaptic input is manipulated pharmacologically or estimated by an alternative method. The method gives best results if the synaptic input is large compared with other conductances, the intrinsic conductances have little or no time dependence or are comparably small, the ligand-gated kinetics is faster than the membrane time constant, and the majority of synaptic contacts are electrotonically close to soma (recording site). Although our data are in current clamp, the method also works in V-clamp recordings, with some minor adaptations. All custom made procedures are provided in Matlab.