| 1. |
- Bizzotto, Roberto, et al.
(författare)
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Multinomial logistic estimation of Markov-chain models for modeling sleep architecture in primary insomnia patients
- 2010
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Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 37:2, s. 137-155
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Tidskriftsartikel (refereegranskat)abstract
- Hypnotic drug development calls for a better understanding of sleep physiology in order to improve and differentiate novel medicines for the treatment of sleep disorders. On this basis, a proper evaluation of polysomnographic data collected in clinical trials conducted to explore clinical efficacy of novel hypnotic compounds should include the assessment of sleep architecture and its drug-induced changes. This work presents a non-linear mixed-effect Markov-chain model based on multinomial logistic functions which characterize the time course of transition probabilities between sleep stages in insomniac patients treated with placebo. Polysomnography measurements were obtained from patients during one night treatment. A population approach was used to describe the time course of sleep stages (awake stage, stage 1, stage 2, slow-wave sleep and REM sleep) using a Markov-chain model. The relationship between time and individual transition probabilities between sleep stages was modelled through piecewise linear multinomial logistic functions. The identification of the model produced a good adherence of mean post-hoc estimates to the observed transition frequencies. Parameters were generally well estimated in terms of CV, shrinkage and distribution of empirical Bayes estimates around the typical values. The posterior predictive check analysis showed good consistency between model-predicted and observed sleep parameters. In conclusion, the Markov-chain model based on multinomial logistic functions provided an accurate description of the time course of sleep stages together with an assessment of the probabilities of transition between different stages.
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| 2. |
- Bizzotto, Roberto, et al.
(författare)
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Multinomial Logistic Functions in Markov Chain Models of Sleep Architecture : Internal and External Validation and Covariate Analysis
- 2011
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Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 13:3, s. 445-463
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Tidskriftsartikel (refereegranskat)abstract
- Mixed-effect Markov chain models have been recently proposed to characterize the time course of transition probabilities between sleep stages in insomniac patients. The most recent one, based on multinomial logistic functions, was used as a base to develop a final model combining the strengths of the existing ones. This final model was validated on placebo data applying also new diagnostic methods and then used for the inclusion of potential age, gender, and BMI effects. Internal validation was performed through simplified posterior predictive check (sPPC), visual predictive check (VPC) for categorical data, and new visual methods based on stochastic simulation and estimation and called visual estimation check (VEC). External validation mainly relied on the evaluation of the objective function value and sPPC. Covariate effects were identified through stepwise covariate modeling within NONMEM VI. New model features were introduced in the model, providing significant sPPC improvements. Outcomes from VPC, VEC, and external validation were generally very good. Age, gender, and BMI were found to be statistically significant covariates, but their inclusion did not improve substantially the model's predictive performance. In summary, an improved model for sleep internal architecture has been developed and suitably validated in insomniac patients treated with placebo. Thereafter, covariate effects have been included into the final model.
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| 3. |
- Allesøe, Rosa Lundbye, et al.
(författare)
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Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models
- 2023
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Ingår i: Nature Biotechnology. - : Springer Nature. - 1087-0156 .- 1546-1696. ; 41:3, s. 399-408
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Tidskriftsartikel (refereegranskat)abstract
- The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug–omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug–drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.
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| 4. |
- Alsalim, Wathik, et al.
(författare)
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Effect of single-dose DPP-4 inhibitor sitagliptin on β-cell function and incretin hormone secretion after meal ingestion in healthy volunteers and drug-naïve, well-controlled type 2 diabetes subjects
- 2018
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Ingår i: Diabetes, Obesity and Metabolism. - : Wiley. - 1462-8902. ; 20:4, s. 1080-1085
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Tidskriftsartikel (refereegranskat)abstract
- To explore the effects of a single dose of the DPP-4 inhibitor sitagliptin on glucose-standardized insulin secretion and β-cell glucose sensitivity after meal ingestion, 12 healthy and 12 drug-naïve, well-controlled type 2 diabetes (T2D) subjects (mean HbA1c 43mmol/mol, 6.2%) received sitagliptin (100mg) or placebo before a meal (525kcal). β-cell function was measured as the insulin secretory rate at a standardized glucose concentration and the β-cell glucose sensitivity (the slope between glucose and insulin secretory rate). Incretin levels were also monitored. Sitagliptin increased standardized insulin secretion, in both healthy and T2D subjects, compared to placebo, but without increasing β-cell glucose sensitivity. Sitagliptin also increased active glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) and reduced total (reflecting the secretion) GIP, but not total GLP-1 levels. We conclude that a single dose of DPP-4 inhibition induces dissociated effects on different aspects of β-cell function and incretin hormones after meal ingestion in both healthy and well-controlled T2D subjects.
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| 5. |
- Alsalim, Wathik, et al.
(författare)
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Incretin and islet hormone responses to meals of increasing size in healthy subjects.
- 2015
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Ingår i: Journal of Clinical Endocrinology and Metabolism. - : The Endocrine Society. - 1945-7197 .- 0021-972X. ; 100:2, s. 561-568
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Tidskriftsartikel (refereegranskat)abstract
- Context: Postprandial glucose homeostasis is regulated through the secretion of glucagon-like peptide 1 (GLP-1) through stimulation of insulin secretion and inhibition of glucagon secretion. However, how these processes dynamically adapt to demands created by caloric challenges achieved during daily life is not known. Objective: To explore adaptation of incretin and islet hormones after mixed meals of increasing size in healthy subjects. Design: Twenty-four healthy lean subjects ingested a standard breakfast after an overnight fast followed, after four hours, by a lunch of different size (511, 743 and 1034 kcal) but with identical nutrient composition together with 1.5g paracetamol. Glucose, insulin, C-peptide, glucagon, intact glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) and paracetamol were measured after the meals. Main outcome measure: Area under the 180 min curve (AUC) for insulin, C-peptide, glucagon, GLP-1 and GIP and model- derived ß-cell function and paracetamol appearance. Results: Glucose profiles were similar after the two larger meals whereas after the smaller meal, there was a post-peak reduction below baseline to nadir of 3.8±0.1mmol/l after 75min (p<0.001). AUC for GLP-1, GIP, insulin and C-peptide were significantly higher by increasing the caloric load as was β-cells sensitivity to glucose. In contrast, AUC glucagon was the same for all three meals, although there was an increase in glucagon after the postpeak glucose reduction in the smaller meal. The 0-20 min paracetamol appearance was increased by increasing meal size. Conclusion: Mixed lunch meals of increasing size elicit a caloric dependent insulin response due to increased β-cell secretion achieved by increased GIP and GLP-1 levels. The adaptation at larger meals results in identical glucose excursions, whereas after a lower caloric lunch the insulin response is high resulting in postpeak suppression of glucose below baseline.
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| 6. |
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| 7. |
- Bizzotto, Roberto, et al.
(författare)
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Processes Underlying Glycemic Deterioration in Type 2 Diabetes : An IMI DIRECT Study
- 2021
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Ingår i: Diabetes Care. - : American Diabetes Association. - 1935-5548 .- 0149-5992. ; 44:2, s. 511-518
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: We investigated the processes underlying glycemic deterioration in type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: A total of 732 recently diagnosed patients with T2D from the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) study were extensively phenotyped over 3 years, including measures of insulin sensitivity (OGIS), β-cell glucose sensitivity (GS), and insulin clearance (CLIm) from mixed meal tests, liver enzymes, lipid profiles, and baseline regional fat from MRI. The associations between the longitudinal metabolic patterns and HbA1c deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression. RESULTS: Faster HbA1c progression was independently associated with faster deterioration of OGIS and GS and increasing CLIm; visceral or liver fat, HDL-cholesterol, and triglycerides had further independent, though weaker, roles (R2 = 0.38). A subgroup of patients with a markedly higher progression rate (fast progressors) was clearly distinguishable considering these variables only (discrimination capacity from area under the receiver operating characteristic = 0.94). The proportion of fast progressors was reduced from 56% to 8-10% in subgroups in which only one trait among OGIS, GS, and CLIm was relatively stable (odds ratios 0.07-0.09). T2D polygenic risk score and baseline pancreatic fat, glucagon-like peptide 1, glucagon, diet, and physical activity did not show an independent role. CONCLUSIONS: Deteriorating insulin sensitivity and β-cell function, increasing insulin clearance, high visceral or liver fat, and worsening of the lipid profile are the crucial factors mediating glycemic deterioration of patients with T2D in the initial phase of the disease. Stabilization of a single trait among insulin sensitivity, β-cell function, and insulin clearance may be relevant to prevent progression.
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| 8. |
- Eriksen, Rebeca, et al.
(författare)
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Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk : An IMI DIRECT study
- 2020
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Ingår i: EBioMedicine. - : Elsevier BV. - 2352-3964. ; 58
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Tidskriftsartikel (refereegranskat)abstract
- Background: Dietary advice remains the cornerstone of prevention and management of type 2 diabetes (T2D). However, understanding the efficacy of dietary interventions is confounded by the challenges inherent in assessing free living diet. Here we profiled dietary metabolites to investigate glycaemic deterioration and cardiometabolic risk in people at risk of or living with T2D. Methods: We analysed data from plasma collected at baseline and 18-month follow-up in individuals from the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohort 1 n = 403 individuals with normal or impaired glucose regulation (prediabetic) and cohort 2 n = 458 individuals with new onset of T2D. A dietary metabolite profile model (Tpred) was constructed using multivariable regression of 113 plasma metabolites obtained from targeted metabolomics assays. The continuous Tpred score was used to explore the relationships between diet, glycaemic deterioration and cardio-metabolic risk via multiple linear regression models. Findings: A higher Tpred score was associated with healthier diets high in wholegrain (β=3.36 g, 95% CI 0.31, 6.40 and β=2.82 g, 95% CI 0.06, 5.57) and lower energy intake (β=-75.53 kcal, 95% CI -144.71, -2.35 and β=-122.51 kcal, 95% CI -186.56, -38.46), and saturated fat (β=-0.92 g, 95% CI -1.56, -0.28 and β=–0.98 g, 95% CI -1.53, -0.42 g), respectively for cohort 1 and 2. In both cohorts a higher Tpred score was also associated with lower total body adiposity and favourable lipid profiles HDL-cholesterol (β=0.07 mmol/L, 95% CI 0.03, 0.1), (β=0.08 mmol/L, 95% CI 0.04, 0.1), and triglycerides (β=-0.1 mmol/L, 95% CI -0.2, -0.03), (β=-0.2 mmol/L, 95% CI -0.3, -0.09), respectively for cohort 1 and 2. In cohort 2, the Tpred score was negatively associated with liver fat (β=-0.74%, 95% CI -0.67, -0.81), and lower fasting concentrations of HbA1c (β=-0.9 mmol/mol, 95% CI -1.5, -0.1), glucose (β=-0.2 mmol/L, 95% CI -0.4, -0.05) and insulin (β=-11.0 pmol/mol, 95% CI -19.5, -2.6). Longitudinal analysis showed at 18-month follow up a higher Tpred score was also associated lower total body adiposity in both cohorts and lower fasting glucose (β=-0.2 mmol/L, 95% CI -0.3, -0.01) and insulin (β=-9.2 pmol/mol, 95% CI -17.9, -0.4) concentrations in cohort 2. Interpretation: Plasma dietary metabolite profiling provides objective measures of diet intake, showing a relationship to glycaemic deterioration and cardiometabolic health. Funding: This work was supported by the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115,317 (DIRECT), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007–2013) and EFPIA companies.
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| 9. |
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| 10. |
- Smith, Mike K., et al.
(författare)
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Model Description Language (MDL) : A Standard for Modeling and Simulation
- 2017
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Ingår i: CPT. - : WILEY. - 2163-8306. ; 6:10, s. 647-650
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Tidskriftsartikel (refereegranskat)abstract
- Recent work on Model Informed Drug Discovery and Development (MID3) has noted the need for clarity in model description used in quantitative disciplines such as pharmacology and statistics. 1-3 Currently, models are encoded in a variety of computer languages and are shared through publications that rarely include original code and generally lack reproducibility. The DDMoRe Model Description Language (MDL) has been developed primarily as a language standard to facilitate sharing knowledge and understanding of models.
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