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Background A key feature of metabolic health is the ability to adapt upon dietary perturbations. Recently, it was shown that metabolic challenge tests in combination with the new generation biomarkers allow the simultaneous quantification of major metabolic health processes. Currently, applied challenge tests are largely non-standardized. A systematic review defined an optimal nutritional challenge test, the “PhenFlex test” (PFT). This study aimed to prove that PFT modulates all relevant processes governing metabolic health thereby allowing to distinguish subjects with different metabolic health status. Therefore, 20 healthy and 20 type 2 diabetic (T2D) male subjects were challenged both by PFT and oral glucose tolerance test (OGTT). During the 8-h response time course, 132 parameters were quantified that report on 26 metabolic processes distributed over 7 organs (gut, liver, adipose, pancreas, vasculature, muscle, kidney) and systemic stress. Results In healthy subjects, 110 of the 132 parameters showed a time course response. Patients with T2D showed 18 parameters to be significantly different after overnight fasting compared to healthy subjects, while 58 parameters were different in the post-challenge time course after the PFT. This demonstrates the added value of PFT in distinguishing subjects with different health status. The OGTT and PFT response was highly comparable for glucose metabolism as identical amounts of glucose were present in both challenge tests. Yet the PFT reports on additional processes, including vasculature, systemic stress, and metabolic flexibility. Conclusion The PFT enables the quantification of all relevant metabolic processes involved in maintaining or regaining homeostasis of metabolic health. Studying both healthy subjects and subjects with impaired metabolic health showed that the PFT revealed new processes laying underneath health. This study provides the first evidence towards adopting the PFT as gold standard in nutrition research. Electronic supplementary material The online version of this article (10.1186/s12263-017-0570-6) contains supplementary material, which is available to authorized users.

Background. N-of-1 designs gain popularity in nutritional research because of the improving technological possibilities, practical applicability and promise of increased accuracy and sensitivity, especially in the field of personalized nutrition. This move asks for a search of applicable statistical methods. Objective. To demonstrate the differences of three popular statistical methods in analyzing treatment effects of data obtained in N-of-1 designs. Method. We compare Individual-participant data meta-analysis, frequentist and Bayesian linear mixed effect models using a simulation experiment. Furthermore, we demonstrate the merits of the Bayesian model including prior information by analyzing data of an empirical study on weight loss. Results. The linear mixed effect models are to be preferred over the meta-analysis method, since the individual effects are estimated more accurately as evidenced by the lower errors, especially with lower sample sizes. Differences between Bayesian and frequentist mixed models were found to be small, indicating that they will lead to the same results without including an informative prior. Conclusion. For empirical data, the Bayesian mixed model allows the inclusion of prior knowledge and gives potential for population based and personalized inference.

Aims A very low calorie diet improves the metabolic regulation of obesity related type 2 diabetes, but not for all patients, which leads to frustration in patients and professionals alike. The aim of this study was to develop a prediction model of diet-induced weight loss in type 2 diabetes. Methods 192 patients with type 2 diabetes and BMI>27 kg/m2 from the outpatient diabetes clinic of the Erasmus Medical Center underwent an 8-week very low calorie diet. Baseline demographic, psychological and physiological parameters were measured and the C-index was calculated of the model with the largest explained variance of relative weight loss using backward linear regression analysis. The model was internally validated using bootstrapping techniques. Results Weight loss after the diet was 7.8±4.6 kg (95%CI 7.2-8.5;p

Introduction Continuous trial outcomes are often dichotomized into ‘response’ and ‘non-response’ categories prior to statistical analysis. This facilitates the interpretation of results, but generally reduces statistical power. Exceptions may occur when response in the study population is heterogeneous, and outcomes are bimodally distributed. We explore whether bimodality is present in antidepressant trial data and whether dichotomizing then indeed results in more powerful statistical tests. Methods The distributions of relative changes from baseline (rCFB) on the Hamilton depression rating scale (HAM-D) were estimated using pooled data from nine antidepressant trials. T-tests on rCFB scores and chi-square tests on dichotomized outcomes were compared to assess the consequences of dichotomization, using both the commonly applied cutoff (i.e. rCFB > 50%) and an estimated cutoff that provided optimal separation of the mixture of two normal distributions that best fitted the pooled placebo outcomes. The power of both tests was also evaluated for simulated scenario's that varied the degree of bimodality and the treatment effect and sample size. Results Placebo and treatment groups showed evidence of bimodality. The estimated cutoff closely matched the commonly applied cutoff. Nevertheless, t-tests generally yielded smaller p-values than chi-square tests. Simulations showed that dichotomization only provides superior power when bimodality was considerably more marked than observed in the empirical data. Conclusion Antidepressant trial outcomes showed bimodality, suggesting differential response among patient groups. This heterogeneity in outcome distributions should be reported more often, since a comparison of means does not adequately summarize the differences between treatment groups. However, simply dichotomizing outcomes is not an appropriate alternative as it reduces statistical power.

This study used arterial spin labeling (ASL) fMRI to measure brain perfusion in a group of healthy men under conditions that closely resembled customary sexual behavior. Serial perfusion measures for 30 min during two self-limited periods of partnered penis stimulation, and during post-stimulatory periods, revealed novel sexual activity-related cerebral blood flow (rCBF) changes, mainly in subcortical parts of the brain. Ventral pallidium rCBF was highest during the onset of penile erection, and lowest after the termination of penis stimulation. The perceived level of sexual arousal showed the strongest positive association with rCBF in the right basal forebrain. in addition, our results demonstrate that distinct subregions of the hypothalamus and cingulate cortex subserve opposite functions during human male sexual behavior. The lateral hypothalamus and anterior part of the middle cingulate cortex showed increased rCBF correlated with penile erection. By contrast, the anteroventral hypothalamus and subgenual anterior cingulate cortex exhibited rCBF changes correlated with penile detumescence after penile stimulation. Continuous rapid and high-resolution brain perfusion imaging during normal sexual activity has provided novel insights into the central mechanisms that control male sexual arousal. (C) 2009 Elsevier Inc. All rights reserved.

Background The sequential parallel comparison (SPC) design was proposed to improve the efficiency of psychiatric clinical trials by reducing the impact of placebo response. It consists of two consecutive placebo-controlled comparisons of which the second is only entered by placebo nonresponders from the first. Previous studies suggest that in antidepressant trials, nonresponse to placebo can already be predicted after 2 weeks of follow-up. This would allow to reduce the first phase of the SPC design to further increase its efficiency. Purpose To compare the sample size requirements of an 8-week randomized controlled trial (RCT8) and alternative SPC designs with equal or longer total follow-up duration (SPC2+6, SPC4+4, and SPC6+6). Methods Scenarios for response and dropout rates were defined. Sample sizes to achieve 80% power were determined for the various designs. Three treatment functions assumed either a smaller, equal, or larger effect at the early stage of the trial as compared with that at the end. Two dropout models described either predominantly early or linearly increasing dropout, and dropout was considered as nonresponse. The relative efficiency of the different designs was evaluated across these scenarios and for a specific scenario based on empirical antidepressant trial data. Results The different SPC designs (i.e., SPC2+6, SPC4+4, and SPC6+6) were generally more efficient than the RCT8 design when the treatment effect at the early stage of the trial was equal or larger than the effect at the end. In this case, the advantage of the SPC designs increased in the presence of dropout. The SPC2+6 design was usually more efficient than the SPC4+4 design and was relatively less affected by dropout when it occurred predominantly early. For the scenario that was based on antidepressant trial data, the SPC2+6 and SPC4+4 designs required 51% and 53% fewer patients than the RCT8 design. Limitations A limited variety of scenarios was evaluated. Parameter values resembled those observed in antidepressant trials. Conclusions This study suggests that SPC designs are highly efficient alternatives to a conventional RCT in indications where placebo response is high and substantial treatment effects are established after a relatively short follow-up period (i.e., after the first SPC design phase). We conclude that SPC designs can reduce sample size requirements and increase success rates of antidepressant trials.

Background The 17-item Hamilton depression rating scale (HAMD17) is the standard efficacy outcome in antidepressant clinical trials. It is criticized for multidimensionality and poorly discriminating treatment from placebo. HAMD subscales may overcome these limitations and reduce the sample size of clinical trials. This study compared the discriminative performance of the HAMD17 and three established HAMD subscales (Bech, Maier-Philipp, Gibbons) across a range of antidepressants with different mechanisms of action. Methods We analyzed data from 24 clinical trials including 3692 patients randomized to tricyclic or tetracyclic antidepressants (TCAs or TeCAs), selective serotonin reuptake inhibitors (SSRIs) or placebo. Data were analyzed using a mixed model for repeated measurements (MMRM). Standardized effect sizes for the HAMD17 and subscales were derived for every time-point, and their effect on sample size was evaluated. Results For TCAs and TeCAs vs. placebo, the HAMD17 consistently provided the highest standardized effects. The sample size to establish efficacy at week six was >25 percent smaller than for any of the subscales. However, for SSRIs vs. placebo, the HAMD17 provided slightly smaller standardized effects and was the least efficient outcome. There were no relevant differences between the subscales. Limitations Data were derived exclusively from mirtazapine trials. Conclusions are restricted to clinical trial settings. Conclusions Comparative performance of the HAMD17 and various subscales strongly depends on type of antidepressant. Results support using HAMD17 as primary endpoint in clinical trials, but it will be beneficial to pro-actively include subscales as additional endpoints to successfully establish treatment effects of new antidepressants.

Two-stage clinical trial designs may be efficient in pharmacogenetics research when there is some but inconclusive evidence of effect modification by a genomic marker. Two-stage designs allow to stop early for efficacy or futility and can offer the additional opportunity to enrich the study population to a specific patient subgroup after an interim analysis. This study compared sample size requirements for fixed parallel group, group sequential, and adaptive selection designs with equal overall power and control of the family-wise type I error rate. The designs were evaluated across scenarios that defined the effect sizes in the marker positive and marker negative subgroups and the prevalence of marker positive patients in the overall study population. Effect sizes were chosen to reflect realistic planning scenarios, where at least some effect is present in the marker negative subgroup. In addition, scenarios were considered in which the assumed ‘true’ subgroup effects (i.e., the postulated effects) differed from those hypothesized at the planning stage. As expected, both two-stage designs generally required fewer patients than a fixed parallel group design, and the advantage increased as the difference between subgroups increased. The adaptive selection design added little further reduction in sample size, as compared with the group sequential design, when the postulated effect sizes were equal to those hypothesized at the planning stage. However, when the postulated effects deviated strongly in favor of enrichment, the comparative advantage of the adaptive selection design increased, which precisely reflects the adaptive nature of the design. Copyright © 2013 John Wiley & Sons, Ltd.

Objectives A multidimensional intervention aimed at reducing silica exposure in the Dutch construction industry was performed. The objective of this study was to assess the effect of the achieved reduction in exposure on the burden of chronic obstructive pulmonary disease (COPD) in construction workers. Method The intervention aimed at technical, organisational and psycho-social factors and was performed in four construction companies. Pre and post intervention respirable quartz exposure measurements were taken in these and four additional control companies. A mathematical simulation model was used to generate COPD prevalences (GOLD stage ≥1) in response to silica exposure, population characteristics and Dutch trends in smoking behaviour for a population of 20–65 year old construction workers with lifetime silica exposure. Results Pre-intervention exposure assessment demonstrated highest respirable quartz levels (mg/m 3 ) for concrete drillers (GM: 0.20, GSD: 2.75), tuck pointers (GM: 0.18, GSD (2.18) and demolishers (GM: 0.12, GSD: 2.86), exceeding the Dutch occupational exposure limit (OEL) in 71, 92 and 97% of cases, respectively. Preliminary simulations estimated COPD prevalence at 21, 20 and 17% for these groups respectively, as compared to 14% when quartz exposure is reduced to the Dutch OEL and 8% with no exposure. Conclusions For several job categories high exposure levels exceeding the Dutch OEL were observed. Reducing these levels to below the OEL would lead to a substantial reduction in the burden of disease. The post intervention exposure levels will become available early 2014. The effect on the burden of disease and economic impact will be assessed with an refined model incorporating population dynamics.

Background: In many therapeutic areas, individual patient markers have been identified that are associated with differential treatment response. These markers include both baseline characteristics, as well as short-term changes following treatment. Using such predictive markers to select subjects for inclusion in randomized clinical trials could potentially result in more targeted studies and reduce the number of subjects to recruit.Methods: This study compared three trial designs on the sample size needed to establish treatment efficacy across a range of realistic scenarios. A conventional parallel group design served as the point of reference, while the alternative designs selected subjects on either a baseline characteristic or an early improvement after a short active run-in phase. Data were generated using a model that characterized the effect of treatment on survival as a combination of a primary effect, an interaction with a baseline marker and/or an early marker improvement. A representative scenario derived from empirical data was also evaluated.Results: Simulations showed that an active run-in design could substantially reduce the number of subjects to recruit when improvement during active run-in was a reliable predictor of differential treatment response. In this case, the baseline selection design was also more efficient than the parallel group design, but less efficient than the active run-in design with an equally restricted population. For most scenarios, however, the advantage of the baseline selection design was limited.Conclusions: An active run-in design could substantially reduce the number of subjects to recruit in a randomized clinical trial. However, just as with the baseline selection design, generalizability of results may be limited and implementation could be difficult.