83 research outputs found
Blood pressure management in type 2 diabetes: integrating clinical trials and genetic data
Background: Blood pressure lowering is an established strategy for preventing microvascular and macrovascular complications of diabetes, but its role in the prevention of diabetes itself is unclear. On the other hand, controversy exists as to whether the threshold of blood pressure for initiation of antihypertensive therapy should differ between people with and without type 2 diabetes. I aimed to integrate individual participant data from major randomised controlled trials and genetic data to fill these knowledge gaps.
Objectives: This thesis sought to examine three main objectives: to investigate the effect of pharmacological blood pressure-lowering on the risk of new-onset type 2 diabetes; to investigate the separate effects of blood pressure-lowering drug classes on the risk of new-onset type 2 diabetes; to investigate the effect of pharmacological blood pressure-lowering treatment for the prevention of major cardiovascular disease in persons with and without type 2 diabetes.
Methods: For the first and second objectives, I conducted a one-stage individual participant-level data meta-analysis of major randomised controlled trials using data from the Blood Pressure Lowering Treatment Trialists' Collaboration (BPLTTC). Analyses were complemented with Mendelian randomisation studies using naturally randomised genetic variants associated with systolic blood pressure and genetic variants in the gene that encodes the therapeutic targets of each drug class. For the third objective, I used one-stage individual participant-level data meta-analysis using the BPLTTC dataset. I expressed the treatment effect per 5 mmHg reduction in systolic blood pressure on the risk of developing a major cardiovascular event as the primary outcome, defined as the first occurrence of fatal or non-fatal stroke or cerebrovascular disease, fatal or non-fatal ischaemic heart disease, or heart failure causing death or requiring hospitalisation. Cox proportional hazard models, stratified by trial, were used to estimate hazard ratios (HRs) separately by type 2 diabetes status at baseline, with further stratification by baseline categories of systolic blood pressure (in 10 mmHg increments from <120 mmHg to ≥170 mmHg).
Results: For the first and second objectives, blood pressure-lowering treatment was found to reduce the risk of diabetes by 11% (hazard ratio per 5 mmHg lower systolic blood pressure 0.89 [95% confidence interval [CI] 0.84 to 0.95]). Similarly, in the Mendelian randomisation study, each 5 mmHg genetically influenced lower systolic blood pressure was associated with an 11% lower risk of diabetes (odds ratio 0.89 [95% CI 0.86 to 0.93]). Evidence from genetic data and trials was also consistent in that angiotensin-converting enzyme inhibitors and angiotensin receptor blockers reduced the risk of diabetes, and beta-blockers increased this risk. There was no effect for calcium channel blockers and findings for thiazide diuretics were inconsistent. For the third objective, over 4.2 years median follow-up (IQR 3.0 to 5.0), a 5 mmHg reduction in systolic blood pressure decreased the risk of major cardiovascular events in both groups, but with a weaker relative treatment effect in participants with type 2 diabetes (HR 0.94 [95% CI 0.91 to 0.98]) compared with those without type 2 diabetes (0.89 [0.87 to 0.92]; p for interaction=0.001). However, absolute risk reductions did not differ substantially between people with and without type 2 diabetes (absolute risk reduction -1.54 [95% CI -2.04 to -1.04] in people with diabetes and -1.61 [-1.86 to -1.36] in people without diabetes, p for interaction =1). We found no reliable evidence for heterogeneity of treatment effects by baseline systolic blood pressure in either group.
Conclusions: Blood pressure lowering is an effective strategy for the prevention of new-onset type 2 diabetes. Established pharmacological interventions, however, have qualitatively and quantitively different effects on diabetes, likely due to their differing off-target effects, with angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers having the most favourable outcomes. Additionally, although the relative beneficial effects of blood pressure reduction on major cardiovascular events were weaker in participants with type 2 diabetes than in those without, absolute effects were similar. The difference in relative risk reduction was not related to the baseline blood pressure or allocation to different drug classes. Therefore, the adoption of differential blood pressure thresholds, intensities of blood pressure lowering, or drug classes used in people with and without type 2 diabetes is not warranted
Effects of blood pressure lowering drugs in heart failure:a systematic review and meta-analysis of randomised controlled trials
We aimed to combine evidence from all heart failure trials that have investigated the effects of drugs with blood pressure (BP)-lowering properties to assess the extent to which such drugs reduce BP in heart failure, the association between the net change in BP between treatment arms and cause-specific outcomes and whether treatment effects (efficacy and safety) vary according to baseline BP. We conducted a systematic review and metaanalysis including randomized clinical trials of drugs with BP-lowering properties in patients with chronic heart failure with at least 300 patient-years follow-up. We included a total of 37 trials (91 950 patients) and showed that treatment with drugs with BP-lowering properties resulted in a small but significant decrease in SBP in patients with heart failure with no evidence that the efficacy and safety of those drugs varied according to baseline BP
Genetic susceptibility, elevated blood pressure and risk of atrial fibrillation:A Mendelian randomization study
Background: Whether elevated blood pressure (BP) is a modifiable risk factor for atrial fibrillation (AF) is not established. We tested (1) whether the association between BP and risk of AF is causal, (2) whether it varies according to individual’s genetic susceptibility for AF, and (3) the extent to which specific BP-lowering drugs are expected to reduce this risk.
Methods: First, causality of association was assessed through two-sample Mendelian randomization, using data from two independent genome-wide association studies that included a total of one million European population. Second, the UK Biobank data of 329,237 participants at baseline was used to study the effect of BP on AF according to genetic susceptibility of developing AF. Third, a possible treatment effect with major BP-lowering drug classes on AF risk was predicted through genetic variants in genes encode the therapeutic targets of each drug class. Estimated drug effects were compared with effects on incident coronary heart disease, for which direct trial evidence exists.
Results: The two-sample Mendelian randomization analysis indicated that, on average, exposure to a higher systolic BP increased the risk of AF by 19% (odds ratio per each 10-mm Hg [OR]: 1.19 [1.12 to 1.27]). This association was replicated in the UK biobank using individual participant data. However, in a further genetic risk-stratified analysis, there was evidence for a linear gradient in the relative effects of systolic BP on AF; while there was no conclusive evidence of an effect in those with low genetic risk, a strong effect was observed among those with high genetic susceptibility for AF. The comparison of predicted treatment effects using genetic proxies for three main drug classes (angiotensin-converting enzyme inhibitors, beta-blockers and calcium channel blockers) suggested similar average effects for the prevention of atrial fibrillation and coronary heart disease.
Conclusions: The effect of elevated BP on the risk of AF is likely to be causal, suggesting that BP-lowering treatment may be effective in AF prevention. However, average effects masked clinically important variations, with a more pronounced effect in individuals with high genetic susceptibility risk for AF
Stratification of diabetes in the context of comorbidities, using representation learning and topological data analysis
Diabetes is a heterogenous, multimorbid disorder with a large variation in manifestations, trajectories, and outcomes. The aim of this study is to validate a novel machine learning method for the phenotyping of diabetes in the context of comorbidities. Data from 9967 multimorbid patients with a new diagnosis of diabetes were extracted from Clinical Practice Research Datalink. First, using BEHRT (a transformer-based deep learning architecture), the embeddings corresponding to diabetes were learned. Next, topological data analysis (TDA) was carried out to test how different areas in high-dimensional manifold correspond to different risk profiles. The following endpoints were considered when profiling risk trajectories: major adverse cardiovascular events (MACE), coronary artery disease (CAD), stroke (CVA), heart failure (HF), renal failure (RF), diabetic neuropathy, peripheral arterial disease, reduced visual acuity and all-cause mortality. Kaplan Meier curves were plotted for each derived phenotype. Finally, we tested the performance of an established risk prediction model (QRISK) by adding TDA-derived features. We identified four subgroups of patients with diabetes and divergent comorbidity patterns differing in their risk of future cardiovascular, renal, and other microvascular outcomes. Phenotype 1 (young with chronic inflammatory conditions) and phenotype 2 (young with CAD) included relatively younger patients with diabetes compared to phenotypes 3 (older with hypertension and renal disease) and 4 (older with previous CVA), and those subgroups had a higher frequency of pre-existing cardio-renal diseases. Within ten years of follow-up, 2592 patients (26%) experienced MACE, 2515 patients (25%) died, and 2020 patients (20%) suffered RF. QRISK3 model’s AUC was augmented from 67.26% (CI 67.25–67.28%) to 67.67% (CI 67.66–67.69%) by adding specific TDA-derived phenotype and the distances to both extremities of the TDA graph improving its performance in the prediction of CV outcomes. We confirmed the importance of accounting for multimorbidity when risk stratifying heterogenous cohort of patients with new diagnosis of diabetes. Our unsupervised machine learning method improved the prediction of clinical outcomes
Inkjet- and flextrail-printing of silicon polymer-based inks for local passivating contacts
In this work innovative additive printing methods for formation of polycrystalline silicon (poly-Si) and polycrystalline silicon carbide (poly-SiC) layers of local tunnel oxide passivating contacts (TOPCon) is evaluated. Replacement of conventional vacuum processes and vapor-phase deposition by additive printing of Si in fabrication process of high efficiency solar cells reduces processing complexity, and, hence manufacturing costs. Reliable inkjet- and FlexTrail-printing processes are developed for liquid-phase polysilane and organic polysilazane inks that are precursors of Si and SiC, respectively. FlexTrail is introduced as a potential technology to print uniform closed thin films of polysilane free of ruptures. Moreover, from inkjet-printing of the developed polysilane ink, homogenous, closed and crack free thin films of poly-Si are obtained after high temperature annealing. The polysilane ink is formulated considering evaluation of several solvents and photoinduced polymerization conditions. Inkjet-printing process development and optimization according to high frequency rheological characterization of organic polysilazane (OPSZ) is presented. Printed thin films are characterized after high temperature annealing (T = 950 °C, t = 60 min) to be uniform and free of micro cracks
Functionalization of polymers and nanomaterials for water treatment, food packaging, textile and biomedical applications: a review
AbstractThe inert nature of most commercial polymers and nanomaterials results in limitations of applications in various industrial fields. This can be solved by surface modifications to improve physicochemical and biological properties, such as adhesion, printability, wetting and biocompatibility. Polymer functionalization allows to graft specific moieties and conjugate molecules that improve material performances. In the last decades, several approaches have been designed in the industry and academia to graft functional groups on surfaces. Here, we review surface decoration of polymers and nanomaterials, with focus on major industrial applications in the medical field, textile industry, water treatment and food packaging. We discuss the advantages and challenges of polymer functionalization. More knowledge is needed on the biology behind cell–polymer interactions, nanosafety and manufacturing at the industrial scale
Blood pressure lowering and risk of new-onset type 2 diabetes:an individual participant data meta-analysis
Background Blood pressure lowering is an established strategy for preventing microvascular and macrovascular complications of diabetes, but its role in the prevention of diabetes itself is unclear. We aimed to examine this question using individual participant data from major randomised controlled trials. Methods We performed a one-stage individual participant data meta-analysis, in which data were pooled to investigate the effect of blood pressure lowering per se on the risk of new-onset type 2 diabetes. An individual participant data network meta-analysis was used to investigate the differential effects of five major classes of antihypertensive drugs on the risk of new-onset type 2 diabetes. Overall, data from 22 studies conducted between 1973 and 2008, were obtained by the Blood Pressure Lowering Treatment Trialists' Collaboration (Oxford University, Oxford, UK). We included all primary and secondary prevention trials that used a specific class or classes of antihypertensive drugs versus placebo or other classes of blood pressure lowering medications that had at least 1000 persons-years of followup in each randomly allocated arm. Participants with a known diagnosis of diabetes at baseline and trials conducted in patients with prevalent diabetes were excluded. For the one-stage individual participant data meta-analysis we used stratified Cox proportional hazards model and for the individual participant data network meta-analysis we used logistic regression models to calculate the relative risk (RR) for drug class comparisons. Findings 145 939 participants (88 500 [60.6%] men and 57 429 [39.4%] women) from 19 randomised controlled trials were included in the one-stage individual participant data meta-analysis. 22 trials were included in the individual participant data network meta-analysis. After a median follow-up of 4.5 years (IQR 2.0), 9883 participants were diagnosed with new-onset type 2 diabetes. Systolic blood pressure reduction by 5 mm Hg reduced the risk of type 2 diabetes across all trials by 11% (hazard ratio 0.89 [95% CI 0.84-0.95]). Investigation of the effects of five major classes of antihypertensive drugs showed that in comparison to placebo, angiotensin-converting enzyme inhibitors (RR 0.84 [95% 0.76-0.93]) and angiotensin II receptor blockers (RR 0.84 [0.76-0.92]) reduced the risk of new-onset type 2 diabetes; however, the use of beta blockers (RR 1.48 [1.27-1.72]) and thiazide diuretics (RR 1.20 [1.07-1.35]) increased this risk, and no material effect was found for calcium channel blockers (RR 1.02 [0.92-1.13]). Interpretation Blood pressure lowering is an effective strategy for the prevention of new-onset type 2 diabetes. Established pharmacological interventions, however, have qualitatively and quantitively different effects on diabetes, likely due to their differing off-target effects, with angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers having the most favourable outcomes. This evidence supports the indication for selected classes of antihypertensive drugs for the prevention of diabetes, which could further refine the selection of drug choice according to an individual's clinical risk of diabetes
Prevalence of Cannabis Lifetime Use in Iranian High School and College Students: A Systematic Review, Meta-Analyses,and Meta-Regression
Cannabis is the most widely used substance in the world. This study aimed to estimate the prevalence of cannabis lifetime use (CLU) in high school and college students of Iran and also to determine factors related to changes in prevalence. A systematic review of literature on cannabis use in Iran was conducted according to MOOSE guideline. Domestic scientific databases, PubMed/Medline, ISI Web of Knowledge, and Google Scholar, relevant reference lists, and relevant journals were searched up to April, 2014. Prevalences were calculated using the variance stabilizing double arcsine transformation and confidence intervals (CIs) estimated using the Wilson method. Heterogeneity was assessed by Cochran's Q statistic and I-2 index and causes of heterogeneity were evaluated using meta-regression model. In electronic database search, 4,000 citations were retrieved, producing a total of 33 studies. CLU was reported with a random effects pooled prevalence of 4.0 (95 CI = 3.0 to 5.0). In subgroups of high school and college students, prevalences were 5.0 (95 CI = 3.0 to -7.0) and 2.0 (95 CI = 2.0 to -3.0), respectively. Meta-regression model indicated that prevalence is higher in college students (beta = 0.089, p < .001), male gender (beta = 0.017, p < .001), and is lower in studies with sampling versus census studies (beta = -0.096, p < .001). This study reported that prevalence of CLU in Iranian students are lower than industrialized countries. In addition, gender, level of education, and methods of sampling are highly associated with changes in the prevalence of CLU across provinces
COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records
BACKGROUND:
Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework.
METHODS:
In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status.
FINDINGS:
Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1.
INTERPRETATION:
Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources.
FUNDING:
British Heart Foundation Data Science Centre, led by Health Data Research UK
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