Diamagnetic repulsion of particles for multilaminar flow assays

© The Royal Society of Chemistry. We demonstrate diamagnetic repulsion forces for performing continuous multilaminar flow assays on particles based on their intrinsic properties and with a simple setup. The platform could be applied to sandwich assays on polystyrene particles, and to cell-based assays via their suspension in biologically benign magnetic media

Effect of statins on venous thromboembolic events: a meta-analysis of published and unpublished evidence from randomised controlled trials

Background - It has been suggested that statins substantially reduce the risk of venous thromboembolic events. We sought to test this hypothesis by performing a meta-analysis of both published and unpublished results from randomised trials of statins. Methods and Findings - We searched MEDLINE, EMBASE, and Cochrane CENTRAL up to March 2012 for randomised controlled trials comparing statin with no statin, or comparing high dose versus standard dose statin, with 100 or more randomised participants and at least 6 months' follow-up. Investigators were contacted for unpublished information about venous thromboembolic events during follow-up. Twenty-two trials of statin versus control (105,759 participants) and seven trials of an intensive versus a standard dose statin regimen (40,594 participants) were included. In trials of statin versus control, allocation to statin therapy did not significantly reduce the risk of venous thromboembolic events (465 [0.9%] statin versus 521 [1.0%] control, odds ratio [OR] = 0.89, 95% CI 0.78–1.01, p = 0.08) with no evidence of heterogeneity between effects on deep vein thrombosis (266 versus 311, OR 0.85, 95% CI 0.72–1.01) and effects on pulmonary embolism (205 versus 222, OR 0.92, 95% CI 0.76–1.12). Exclusion of the trial result that provided the motivation for our meta-analysis (JUPITER) had little impact on the findings for venous thromboembolic events (431 [0.9%] versus 461 [1.0%], OR = 0.93 [95% CI 0.82–1.07], p = 0.32 among the other 21 trials). There was no evidence that higher dose statin therapy reduced the risk of venous thromboembolic events compared with standard dose statin therapy (198 [1.0%] versus 202 [1.0%], OR = 0.98, 95% CI 0.80–1.20, p = 0.87). Risk of bias overall was small but a certain degree of effect underestimation due to random error cannot be ruled out. Please see later in the article for the Editors' Summary. Conclusions - The findings from this meta-analysis do not support the previous suggestion of a large protective effect of statins (or higher dose statins) on venous thromboembolic events. However, a more moderate reduction in risk up to about one-fifth cannot be ruled out

Use of wireline logs at Cerro Prieto in the identification of the distribution of hydrothermally altered zones and dike locations and their correlation with reservoir temperatures

In this paper wells M-43, T-366, and M-107 are discussed in detail as typical cases

Congestion management with aggregated delivery of flexibility using distributed energy resources

Increasing penetrations of small scale electricity generation and storage technologies are making an important contribution to the decentralisation and decarbonisation of power system control and operation. Although not currently realised, coordination of local distributed energy resources (DERs) and a greater degree of demand flexibility through digital aggregation, offer the potential to lower the cost of energy at source and to enable remuneration for consumer participation, addressing the rising costs of energy supply, which impacts strongly on all consumers. Methods are required to manage potential distribution network constraints caused by flexible DERs, as well as for determining the risk to delivery of flexibility from these DERs for aggregators. A heuristic network flexibility dispatch methodology is proposed, which can be used to calculate the probability of constraints, and any required adjustments of flexible agent positions to resolve them, at half hourly resolution. The aggregator can use this methodology to manage their portfolio risk, while a distribution system operator can estimate required flexibility to manage constraints down to low voltage level

Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes

Aims/hypothesis: This study aimed to explore the added value of subgroups that categorise individuals with type 2 diabetes by k-means clustering for two primary care registries (the Netherlands and Scotland), inspired by Ahlqvist’s novel diabetes subgroups and previously analysed by Slieker et al. Methods: We used two Dutch and Scottish diabetes cohorts (N=3054 and 6145; median follow-up=11.2 and 12.3 years, respectively) and defined five subgroups by k-means clustering with age at baseline, BMI, HbA1c, HDL-cholesterol and C-peptide. We investigated differences between subgroups by trajectories of risk factor values (random intercept models), time to diabetes-related complications (logrank tests and Cox models) and medication patterns (multinomial logistic models). We also compared directly using the clustering indicators as predictors of progression vs the k-means discrete subgroups. Cluster consistency over follow-up was assessed. Results: Subgroups’ risk factors were significantly different, and these differences remained generally consistent over follow-up. Among all subgroups, individuals with severe insulin resistance faced a significantly higher risk of myocardial infarction both before (HR 1.65; 95% CI 1.40, 1.94) and after adjusting for age effect (HR 1.72; 95% CI 1.46, 2.02) compared with mild diabetes with high HDL-cholesterol. Individuals with severe insulin-deficient diabetes were most intensively treated, with more than 25% prescribed insulin at 10 years of diagnosis. For severe insulin-deficient diabetes relative to mild diabetes, the relative risks for using insulin relative to no common treatment would be expected to increase by a factor of 3.07 (95% CI 2.73, 3.44), holding other factors constant. Clustering indicators were better predictors of progression variation relative to subgroups, but prediction accuracy may improve after combining both. Clusters were consistent over 8 years with an accuracy ranging from 59% to 72%. Conclusions/interpretation: Data-driven subgroup allocations were generally consistent over follow-up and captured significant differences in risk factor trajectories, medication patterns and complication risks. Subgroups serve better as a complement rather than as a basis for compressing clustering indicators. Graphical Abstract

Magnesium intake and vascular structure and function:the Hoorn Study

PURPOSE: Circulating and dietary magnesium have been shown to be inversely associated with the prevalence of cardiovascular disease (CVD) and mortality in both high and low-risk populations. We aimed to examine the association between dietary magnesium intake and several measures of vascular structure and function in a prospective cohort. METHODS: We included 789 participants who participated in the vascular screening sub-cohort of the Hoorn Study, a population-based, prospective cohort study. Baseline dietary magnesium intake was estimated with a validated food frequency questionnaire and categorised in energy-adjusted magnesium intake tertiles. Several measurements of vascular structure and function were performed at baseline and most measurements were repeated after 8 years of follow-up (n = 432). Multivariable linear and logistic regression was performed to study the cross-sectional and longitudinal associations of magnesium intake and intima-media thickness (IMT), augmentation index (Aix), pulse wave velocity (PWV), flow-mediated dilatation (FMD), and peripheral arterial disease (PAD). RESULTS: Mean absolute magnesium intake was 328 ± 83 mg/day and prior CVD and DM2 was present in 55 and 41% of the participants, respectively. Multivariable regression analyses did not demonstrate associations between magnesium intake and any of the vascular outcomes. Participants in the highest compared to the lowest magnesium intake tertile demonstrated in fully adjusted cross-sectional analyses a PWV of −0.21 m/s (95% confidence interval −1.95, 1.52), a FMD of −0.03% (−0.89, 0.83) and in longitudinal analyses an IMT of 0.01 mm (−0.03, 0.06), an Aix of 0.70% (−1.69, 3.07) and an odds ratio of 0.84 (0.23, 3.11) for PAD CONCLUSION: We did not find associations between dietary magnesium intake and multiple markers of vascular structure and function, in either cross-sectional or longitudinal analyses. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00394-021-02667-0

An omics-based machine learning approach to predict diabetes progression:a RHAPSODY study

Aims/hypothesis: People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA 1c and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin initiation or requirement and whether newly identified markers have added predictive value. Methods: In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest). Clinical variables included age, sex, HbA 1c, HDL-cholesterol and C-peptide. Models were run with unpenalised clinical variables (i.e. always included in the model without weights) or penalised clinical variables, or without clinical variables. Model development was performed in one cohort and the model was applied in a second cohort. Model performance was evaluated using Harrel’s C statistic. Results: Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0–11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3–11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA 1c (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%). Base models (age, sex, BMI, HbA 1c) including only clinical variables performed moderately in both the DCS discovery cohort (C statistic 0.71 [95% CI 0.64, 0.79]) and the GoDARTS replication cohort (C 0.71 [95% CI 0.69, 0.75]). A more extensive model including HDL-cholesterol and C-peptide performed better in both cohorts (DCS, C 0.74 [95% CI 0.67, 0.81]; GoDARTS, C 0.73 [95% CI 0.69, 0.77]). Two proteins, lactadherin and proto-oncogene tyrosine-protein kinase receptor, were most consistently selected and slightly improved model performance. Conclusions/interpretation: Using machine learning approaches, we show that insulin requirement risk can be modestly well predicted by predominantly clinical variables. Inclusion of molecular markers improves the prognostic performance beyond that of clinical variables by up to 5%. Such prognostic models could be useful for identifying people with diabetes at high risk of progressing quickly to treatment intensification. Data availability: Summary statistics of lipidomic, proteomic and metabolomic data are available from a Shiny dashboard at https://rhapdata-app.vital-it.ch. Graphical Abstract: (Figure presented.).</p

Performance of prediction models for nephropathy in people with type 2 diabetes:systematic review and external validation study

OBJECTIVES To identify and assess the quality and accuracy of prognostic models for nephropathy and to validate these models in external cohorts of people with type 2 diabetes. DESIGN Systematic review and external validation. DATA SOURCES PubMed and Embase. ELIGIBILITY CRITERIA Studies describing the development of a model to predict the risk of nephropathy, applicable to people with type 2 diabetes. METHODS Screening, data extraction, and risk of bias assessment were done in duplicate. Eligible models were externally validated in the Hoorn Diabetes Care System (DCS) cohort (n=11 450) for the same outcomes for which they were developed. Risks of nephropathy were calculated and compared with observed risk over 2, 5, and 10 years of follow-up. Model performance was assessed based on intercept adjusted calibration and discrimination (Harrell's C statistic). RESULTS 41 studies included in the systematic review reported 64 models, 46 of which were developed in a population with diabetes and 18 in the general population including diabetes as a predictor. The predicted outcomes included albuminuria, diabetic kidney disease, chronic kidney disease (general population), and end stage renal disease. The reported apparent discrimination of the 46 models varied considerably across the different predicted outcomes, from 0.60 (95% confidence interval 0.56 to 0.64) to 0.99 (not available) for the models developed in a diabetes population and from 0.59 (not available) to 0.96 (0.95 to 0.97) for the models developed in the general population. Calibration was reported in 31 of the 41 studies, and the models were generally well calibrated. 21 of the 64 retrieved models were externally validated in the Hoorn DCS cohort for predicting risk of albuminuria, diabetic kidney disease, and chronic kidney disease, with considerable variation in performance across prediction horizons and models. For all three outcomes, however, at least two models had C statistics >0.8, indicating excellent discrimination. In a secondary external validation in GoDARTS (Genetics of Diabetes Audit and Research in Tayside Scotland), models developed for diabetic kidney disease outperformed those for chronic kidney disease. Models were generally well calibrated across all three prediction horizons. CONCLUSIONS This study identified multiple prediction models to predict albuminuria, diabetic kidney disease, chronic kidney disease, and end stage renal disease. In the external validation, discrimination and calibration for albuminuria, diabetic kidney disease, and chronic kidney disease varied considerably across prediction horizons and models. For each outcome, however, specific models showed good discrimination and calibration across the three prediction horizons, with clinically accessible predictors, making them applicable in a clinical setting. SYSTEMATIC REVIEW REGISTRATION PROSPERO CRD42020192831.Molecular Epidemiolog

The influence of psychosocial factors at work and life style on health and work ability among professional workers

OBJECTIVES: The purpose of this article is to explore the associations of psychosocial factors at work, life style, and stressful life events on health and work ability among white-collar workers. METHODS: A cross-sectional survey was conducted among workers in commercial services (n = 1141). The main outcome variables were work ability, measured by the work ability index (WAI), and mental and physical health, measured by the Short-Form Health Survey (SF-12). Individual characteristics, psychosocial factors at work, stressful life events, and lifestyle factors were determined by a questionnaire. Maximum oxygen uptake, weight, height, and biceps strength were measured during a physical examination. RESULTS: Work ability of white-collar workers in commercial services industry was strongly associated with psychosocial factors at work such as teamwork, stress handling, and self-development and, to a lesser extent, with stressful life events, lack of physical activity, and obesity. Determinants of mental health were very similar to those of work ability, whereas physical health was influenced primarily by life style factors. With respect to work ability, the influence of unhealthy life style seems more important for older workers, than for their younger colleagues. CONCLUSION: Among white-collar workers mental and physical health were of equal importance to work ability, but only mental health and work ability shared the same determinants. The strong associations between psychosocial factors at work and mental health and work ability suggest that in this study population health promotion should address working conditions rather than individual life style factors

Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes:an IMI-RHAPSODY Study

Introduction: Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised “bottom-up” approach, we attempt to group T2D patients based solely on -omics data generated from plasma. Methods: Circulating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics. Results: From a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor. Conclusions: Using an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.</p