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- Koivula, Robert W., et al.
(författare)
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Discovery of biomarkers for glycaemic deterioration before and after the onset of type 2 diabetes : descriptive characteristics of the epidemiological studies within the IMI DIRECT Consortium
- 2019
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Ingår i: Diabetologia. - : Springer. - 0012-186X .- 1432-0428. ; 62:9, s. 1601-1615
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Tidskriftsartikel (refereegranskat)abstract
- Aims/hypothesis: Here, we describe the characteristics of the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) epidemiological cohorts at baseline and follow-up examinations (18, 36 and 48 months of follow-up).Methods: From a sampling frame of 24,682 adults of European ancestry enrolled in population-based cohorts across Europe, participants at varying risk of glycaemic deterioration were identified using a risk prediction algorithm (based on age, BMI, waist circumference, use of antihypertensive medication, smoking status and parental history of type 2 diabetes) and enrolled into a prospective cohort study (n = 2127) (cohort 1, prediabetes risk). We also recruited people from clinical registries with type 2 diabetes diagnosed 6-24 months previously (n = 789) into a second cohort study (cohort 2, diabetes). Follow-up examinations took place at similar to 18 months (both cohorts) and at similar to 48 months (cohort 1) or similar to 36 months (cohort 2) after baseline examinations. The cohorts were studied in parallel using matched protocols across seven clinical centres in northern Europe.Results: Using ADA 2011 glycaemic categories, 33% (n = 693) of cohort 1 (prediabetes risk) had normal glucose regulation and 67% (n = 1419) had impaired glucose regulation. Seventy-six per cent of participants in cohort 1 was male. Cohort 1 participants had the following characteristics (mean +/- SD) at baseline: age 62 (6.2) years; BMI 27.9 (4.0) kg/m(2); fasting glucose 5.7 (0.6) mmol/l; 2 h glucose 5.9 (1.6) mmol/l. At the final follow-up examination the participants' clinical characteristics were as follows: fasting glucose 6.0 (0.6) mmol/l; 2 h OGTT glucose 6.5 (2.0) mmol/l. In cohort 2 (diabetes), 66% (n = 517) were treated by lifestyle modification and 34% (n = 272) were treated with metformin plus lifestyle modification at enrolment. Fifty-eight per cent of participants in cohort 2 was male. Cohort 2 participants had the following characteristics at baseline: age 62 (8.1) years; BMI 30.5 (5.0) kg/m(2); fasting glucose 7.2 (1.4) mmol/l; 2 h glucose 8.6 (2.8) mmol/l. At the final follow-up examination, the participants' clinical characteristics were as follows: fasting glucose 7.9 (2.0) mmol/l; 2 h mixed-meal tolerance test glucose 9.9 (3.4) mmol/l.Conclusions/interpretation: The IMI DIRECT cohorts are intensely characterised, with a wide-variety of metabolically relevant measures assessed prospectively. We anticipate that the cohorts, made available through managed access, will provide a powerful resource for biomarker discovery, multivariate aetiological analyses and reclassification of patients for the prevention and treatment of type 2 diabetes.
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- Aguilar, J., et al.
(författare)
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Study of nonstandard interactions mediated by a scalar field at the ESSnuSB experiment
- 2024
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Ingår i: Physical Review D. - : American Physical Society. - 2470-0010 .- 2470-0029. ; 109:11
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we study scalar mediator induced nonstandard interactions (SNSIs) in the context of the ESSnuSB experiment. In particular, we study the capability of ESSnuSB to put bounds on the SNSI parameters and also study the impact of SNSIs in the measurement of the leptonic CP phase δCP. Existence of SNSIs modifies the neutrino mass matrix and this modification can be expressed in terms of three diagonal real parameters (ηee, ημμ, and ηττ) and three off-diagonal complex parameters (ηeμ, ηeτ, and ημτ). Our study shows that the upper bounds on the parameters ημμ and ηττ depend upon how Δm312 is minimized in the theory. However, this is not the case when one tries to measure the impact of SNSIs on δCP. Further, we show that the CP sensitivity of ESSnuSB can be completely lost for certain values of ηee and ημτ for which the appearance channel probability becomes independent of δCP.
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- Czeiter, Endre, et al.
(författare)
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Blood biomarkers on admission in acute traumatic brain injury : Relations to severity, CT findings and care path in the CENTER-TBI study
- 2020
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Ingår i: EBioMedicine. - : Elsevier. - 2352-3964. ; 56
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Serum biomarkers may inform and improve care in traumatic brain injury (TBI). We aimed to correlate serum biomarkers with clinical severity, care path and imaging abnormalities in TBI, and explore their incremental value over clinical characteristics in predicting computed tomographic (CT) abnormalities.METHODS: We analyzed six serum biomarkers (S100B, NSE, GFAP, UCH-L1, NFL and t-tau) obtained <24 h post-injury from 2867 patients with any severity of TBI in the Collaborative European NeuroTrauma Effectiveness Research (CENTER-TBI) Core Study, a prospective, multicenter, cohort study. Univariable and multivariable logistic regression analyses were performed. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals.FINDINGS: All biomarkers scaled with clinical severity and care path (ER only, ward admission, or ICU), and with presence of CT abnormalities. GFAP achieved the highest discrimination for predicting CT abnormalities (AUC 0•89 [95%CI: 0•87-0•90]), with a 99% likelihood of better discriminating CT-positive patients than clinical characteristics used in contemporary decision rules. In patients with mild TBI, GFAP also showed incremental diagnostic value: discrimination increased from 0•84 [95%CI: 0•83-0•86] to 0•89 [95%CI: 0•87-0•90] when GFAP was included. Results were consistent across strata, and injury severity. Combinations of biomarkers did not improve discrimination compared to GFAP alone.INTERPRETATION: Currently available biomarkers reflect injury severity, and serum GFAP, measured within 24 h after injury, outperforms clinical characteristics in predicting CT abnormalities. Our results support the further development of serum GFAP assays towards implementation in clinical practice, for which robust clinical assay platforms are required.FUNDING: CENTER-TBI study was supported by the European Union 7th Framework program (EC grant 602150).
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- Dijkland, Simone A., et al.
(författare)
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Outcome Prediction after Moderate and Severe Traumatic Brain Injury : External Validation of Two Established Prognostic Models in 1742 European Patients
- 2021
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Ingår i: Journal of Neurotrauma. - : Mary Ann Liebert. - 0897-7151 .- 1557-9042. ; 38:10, s. 1377-1388
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Tidskriftsartikel (refereegranskat)abstract
- The International Mission on Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) and Corticoid Randomisation After Significant Head injury (CRASH) prognostic models predict functional outcome after moderate and severe traumatic brain injury (TBI). We aimed to assess their performance in a contemporary cohort of patients across Europe. The Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) core study is a prospective, observational cohort study in patients presenting with TBI and an indication for brain computed tomography. The CENTER-TBI core cohort consists of 4509 TBI patients available for analyses from 59 centers in 18 countries across Europe and Israel. The IMPACT validation cohort included 1173 patients with GCS ≤12, age ≥14, and 6-month Glasgow Outcome Scale-Extended (GOSE) available. The CRASH validation cohort contained 1742 patients with GCS ≤14, age ≥16, and 14-day mortality or 6-month GOSE available. Performance of the three IMPACT and two CRASH model variants was assessed with discrimination (area under the receiver operating characteristic curve; AUC) and calibration (comparison of observed vs. predicted outcome rates). For IMPACT, model discrimination was good, with AUCs ranging between 0.77 and 0.85 in 1173 patients and between 0.80 and 0.88 in the broader CRASH selection (n = 1742). For CRASH, AUCs ranged between 0.82 and 0.88 in 1742 patients and between 0.66 and 0.80 in the stricter IMPACT selection (n = 1173). Calibration of the IMPACT and CRASH models was generally moderate, with calibration-in-the-large and calibration slopes ranging between -2.02 and 0.61 and between 0.48 and 1.39, respectively. The IMPACT and CRASH models adequately identify patients at high risk for mortality or unfavorable outcome, which supports their use in research settings and for benchmarking in the context of quality-of-care assessment.
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- Gravesteijn, Benjamin Yaël, et al.
(författare)
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Missing Data in Prediction Research : A Five-Step Approach for Multiple Imputation, Illustrated in the CENTER-TBI Study
- 2021
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Ingår i: Journal of Neurotrauma. - : Mary Ann Liebert. - 0897-7151 .- 1557-9042. ; 38:13, s. 1842-1857
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Tidskriftsartikel (refereegranskat)abstract
- In medical research, missing data is common. In acute diseases, such as traumatic brain injury (TBI), even well-conducted prospective studies may suffer from missing data in baseline characteristics and outcomes. Statistical models may simply drop patients with any missing values, potentially leaving a selected subset of the original cohort. Imputation is widely accepted by methodologists as an appropriate way to deal with missing data. We aim to provide practical guidance on handling missing data for prediction modeling. We hereto propose a five-step approach, centered around single and multiple imputation: 1) explore the missing data patterns; 2) choose a method of imputation; 3) perform imputation; 4) assess diagnostics of the imputation; and 5) analyze the imputed data sets. We illustrate these five steps with the estimation and validation of the IMPACT (International Mission on Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury) prognostic model in 1375 patients from the CENTER-TBI database, included in 53 centers across 17 countries, with moderate or severe TBI in the prospective European CENTER-TBI study. Future prediction modeling studies in acute diseases may benefit from following the suggested five steps for optimal statistical analysis and interpretation, after maximal effort has been made to minimize missing data.
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| 10. |
- Gudmundsdottir, Valborg, et al.
(författare)
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Whole blood co-expression modules associate with metabolic traits and type 2 diabetes : an IMI-DIRECT study
- 2020
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Ingår i: Genome Medicine. - : BioMed Central. - 1756-994X. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: The rising prevalence of type 2 diabetes (T2D) poses a major global challenge. It remains unresolved to what extent transcriptomic signatures of metabolic dysregulation and T2D can be observed in easily accessible tissues such as blood. Additionally, large-scale human studies are required to further our understanding of the putative inflammatory component of insulin resistance and T2D. Here we used transcriptomics data from individuals with (n = 789) and without (n = 2127) T2D from the IMI-DIRECT cohorts to describe the co-expression structure of whole blood that mainly reflects processes and cell types of the immune system, and how it relates to metabolically relevant clinical traits and T2D.Methods: Clusters of co-expressed genes were identified in the non-diabetic IMI-DIRECT cohort and evaluated with regard to stability, as well as preservation and rewiring in the cohort of individuals with T2D. We performed functional and immune cell signature enrichment analyses, and a genome-wide association study to describe the genetic regulation of the modules. Phenotypic and trans-omics associations of the transcriptomic modules were investigated across both IMI-DIRECT cohorts.Results: We identified 55 whole blood co-expression modules, some of which clustered in larger super-modules. We identified a large number of associations between these transcriptomic modules and measures of insulin action and glucose tolerance. Some of the metabolically linked modules reflect neutrophil-lymphocyte ratio in blood while others are independent of white blood cell estimates, including a module of genes encoding neutrophil granule proteins with antibacterial properties for which the strongest associations with clinical traits and T2D status were observed. Through the integration of genetic and multi-omics data, we provide a holistic view of the regulation and molecular context of whole blood transcriptomic modules. We furthermore identified an overlap between genetic signals for T2D and co-expression modules involved in type II interferon signaling.Conclusions: Our results offer a large-scale map of whole blood transcriptomic modules in the context of metabolic disease and point to novel biological candidates for future studies related to T2D.
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