Empirical realised niche models for British coastal plant species

Coastal environments host plant taxa adapted to a wide range of salinity conditions. Salinity, along with other abiotic variables, constrains the distribution of coastal plants in predictable ways, with relatively few taxa adapted to the most saline conditions. However, few attempts have been made to quantify these relationships to create niche models for coastal plants. Quantification of the effects of salinity, and other abiotic variables, on coastal plants is essential to predict the responses of coastal ecosystems to external drivers such as sea level rise. We constructed niche models for 132 coastal plant taxa in Great Britain based on eight abiotic variables. Paired measurements of vegetation composition and abiotic variables are rare in coastal habitats so four of the variables were defined using community mean values for Ellenberg indicators, i.e. scores assigned according to the typical alkalinity, fertility, moisture availability and salinity of sites where a species occurs. The remaining variables were the canopy height, annual precipitation, and maximum and minimum temperatures. Salinity and moisture indicator scores were significant terms in over 80 % of models, suggesting the distributions of most coastal species are at least partly determined by these variables. When the models were used to predict species occurrence against an independent dataset 64 % of models gave moderate to good predictions of species occurrence. This indicates that most models had successfully captured the key determinants of the niche. The models could potentially be applied to predict changes to habitats and species-dependent ecosystem services in response to rising sea levels

Evaluating associations between the benefits and risks of drug therapy in type 2 diabetes:A joint modelling approach

This is the author accepted manuscript. The final version is available from Dove Medical Press via the DOI in this record.Data statement: No additional data are available from the authors although the individual participant data from the ADOPT trial used in this study are available from GlaxoSmithKline on application via www.clinicalstudydatarequest.comObjective: Precision medicine drug therapy seeks to maximise efficacy and minimise harm for individual patients. This will be difficult if drug response and side-effects are positively associated, meaning patients likely to respond best are at increased risk of side-effects. We applied joint longitudinal-survival models to evaluate associations between drug response (longitudinal outcome) and risk of side-effects (survival outcome) for patients initiating type 2 diabetes therapy. Study Design and Setting: Participants were randomised to metformin, sulfonylurea or thiazolidinedione therapy in the ADOPT drug-efficacy trial (n=4,351). Joint models were parameterised for: 1) current HbA1c response (change from baseline in HbA1c); 2) cumulative HbA1c response (total HbA1c change). Results: With metformin, greater HbA1c response did not increase risk of gastrointestinal events (Hazard ratio (HR) per 1% absolute greater current response 0.82 (95% confidence interval 0.67,1.01); HR per 1% higher cumulative response 0.90 (0.81,1.00)). With sulfonylureas, greater current response was associated with increased risk of hypoglycaemia (HR 1.41 (1.04,1.91)). With thiazolidinediones, greater response was associated with increased risk of oedema (current HR 1.45 (1.05,2.01); cumulative 1.22 (1.07,1.38)) but not fracture. Conclusion: Joint modelling provides a useful framework to evaluate the association between response to a drug and risk of developing side-effects. There may be great potential for widespread application of joint modelling to evaluate the risks and benefits of both new and established medications.This work was supported by the Medical Research Council (UK) (Grant MR/N00633X/1). ATH is a NIHR Senior Investigator and a Wellcome Trust Senior Investigator. ERP is a Wellcome Trust New Investigator (102820/Z/13/Z). AGJ is supported by an NIHR Clinician Scientist award. ATH and BMS are supported by the NIHR Exeter Clinical Research Facility. WEH received additional support from IQVIA and the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care South West Peninsula (NIHR CLAHRC South West Peninsula)

The phenotype of type 1 diabetes in sub-Saharan Africa

The phenotype of type 1 diabetes in Africa, especially sub-Saharan Africa, is poorly understood. Most previously conducted studies have suggested that type 1 diabetes may have a different phenotype from the classical form of the disease described in western literature. Making an accurate diagnosis of type 1 diabetes in Africa is challenging, given the predominance of atypical diabetes forms and limited resources. The peak age of onset of type 1 diabetes in sub-Saharan Africa seems to occur after 18–20 years. Multiple studies have reported lower rates of islet autoantibodies ranging from 20 to 60% amongst people with type 1 diabetes in African populations, lower than that reported in other populations. Some studies have reported much higher levels of retained endogenous insulin secretion than in type 1 diabetes elsewhere, with lower rates of type 1 diabetes genetic susceptibility and HLA haplotypes. The HLA DR3 appears to be the most predominant HLA haplotype amongst people with type 1 diabetes in sub-Saharan Africa than the HLA DR4 haplotype. Some type 1 diabetes studies in sub-Saharan Africa have been limited by small sample sizes and diverse methods employed. Robust studies close to diabetes onset are sparse. Large prospective studies with well-standardized methodologies in people at or close to diabetes diagnosis in different population groups will be paramount to provide further insight into the phenotype of type 1 diabetes in sub-Saharan Africa

Robotic arm-assisted bi-unicompartmental knee arthroplasty maintains natural knee joint anatomy compared with total knee arthroplasty : a prospective randomized controlled trial

Aims The aim of this study was to compare robotic arm-assisted bi-unicompartmental knee arthroplasty (bi-UKA) with conventional mechanically aligned total knee arthroplasty (TKA) in order to determine the changes in the anatomy of the knee and alignment of the lower limb following surgery. Methods An analysis of 38 patients who underwent TKA and 32 who underwent bi-UKA was performed as a secondary study from a prospective, single-centre, randomized controlled trial. CT imaging was used to measure coronal, sagittal, and axial alignment of the knee preoperatively and at three months postoperatively to determine changes in anatomy that had occurred as a result of the surgery. The hip-knee-ankle angle (HKAA) was also measured to identify any differences between the two groups. Results The pre- to postoperative changes in joint anatomy were significantly less in patients undergoing bi-UKA in all three planes in both the femur and tibia, except for femoral sagittal component orientation in which there was no difference. Overall, for the six parameters of alignment (three femoral and three tibial), 47% of bi-UKAs and 24% TKAs had a change of < 2° (p = 0.045). The change in HKAA towards neutral in varus and valgus knees was significantly less in patients undergoing bi-UKA compared with those undergoing TKA (p < 0.001). Alignment was neutral in those undergoing TKA (mean 179.5° (SD 3.2°)) while those undergoing bi-UKA had mild residual varus or valgus alignment (mean 177.8° (SD 3.4°)) (p < 0.001). Conclusion Robotic-assisted, cruciate-sparing bi-UKA maintains the natural anatomy of the knee in the coronal, sagittal, and axial planes better, and may therefore preserve normal joint kinematics, compared with a mechanically aligned TKA. This includes preservation of coronal joint line obliquity. HKAA alignment was corrected towards neutral significantly less in patients undergoing bi-UKA, which may represent restoration of the pre-disease constitutional alignment (p < 0.001)

Patient preference for second- and third-line therapies in type 2 diabetes:a prespecified secondary endpoint of the TriMaster study

Patient preference is very important for medication selection in chronic medical conditions, like type 2 diabetes, where there are many different drugs available. Patient preference balances potential efficacy with potential side effects. As both aspects of drug response can vary markedly between individuals, this decision could be informed by the patient personally experiencing the alternative medications, as occurs in a crossover trial. In the TriMaster (NCT02653209, ISRCTN12039221), randomized double-blind, three-way crossover trial patients received three different second- or third-line once-daily type 2 diabetes glucose-lowering drugs (pioglitazone 30 mg, sitagliptin 100 mg and canagliflozin 100 mg). As part of a prespecified secondary endpoint, we examined patients’ drug preference after they had tried all three drugs. In total, 448 participants were treated with all three drugs which overall showed similar glycemic control (HbA1c on pioglitazone 59.5 sitagliptin 59.9, canagliflozin 60.5 mmol mol−1, P = 0.19). In total, 115 patients (25%) preferred pioglitazone, 158 patients (35%) sitagliptin and 175 patients (38%) canagliflozin. The drug preferred by individual patients was associated with a lower HbA1c (mean: 4.6; 95% CI: 3.9, 5.3) mmol mol−1 lower versus nonpreferred) and fewer side effects (mean: 0.50; 95% CI: 0.35, 0.64) fewer side effects versus nonpreferred). Allocating therapy based on the individually preferred drugs, rather than allocating all patients the overall most preferred drug (canagliflozin), would result in more patients achieving the lowest HbA1c for them (70% versus 30%) and the fewest side effects (67% versus 50%). When precision approaches do not predict a clear optimal therapy for an individual, allowing patients to try potential suitable medications before they choose long-term therapy could be a practical alternative to optimizing treatment for type 2 diabetes

Understanding the manifestation of diabetes in sub Saharan Africa to inform therapeutic approaches and preventive strategies: a narrative review.

BACKGROUND: Globally, the burden of diabetes mellitus has increased to epidemic proportions. Estimates from the International Diabetes Federation predict that the greatest future increase in the prevalence of diabetes mellitus will occur in Africa. METHODS: This article reviews literature on the manifestation of diabetes in adult patients in sub-Saharan Africa highlighting the distinct phenotypes, plausible explanations for this unique manifestation and the clinical significance of comprehensively defining and understanding the African diabetes phenotype. RESULTS: There are few studies on the manifestation or phenotype of diabetes in Africa. The limited data available suggests that, compared to the Western world, the majority of patients with diabetes in Africa are young and relatively lean in body size. In addition, hyperglycaemia in most cases is characterised by a significantly blunted acute first phase of insulin secretion in response to an oral or intravenous glucose load and pancreatic beta cell secretory dysfunction, rather than peripheral insulin resistance predominates. Genetic and environmental factors like chronic infections/inflammation, early life malnutrition and epigenetic modifications are thought to contribute to these distinct differences in manifestation. CONCLUSIONS: While published data is limited, there appears to be distinct phenotypes of diabetes in sub-Saharan Africa. Large and more detailed studies are needed especially among newly diagnosed patients to fully characterize diabetes in this region. This will further improve the understanding of manifestation of diabetes and guide the formulation of optimal therapeutic approaches and preventive strategies of the condition on the continent

Time trends in prescribing of type 2 diabetes drugs, glycaemic response and risk factors:a retrospective analysis of primary care data, 2010-2017

This is the author accepted manuscript. The final version is available on open access from Wiley via the DOI in this recordAim: Prescribing in type 2 diabetes has changed markedly in recent years, with increasing use of newer, more expensive glucose-lowering drugs. We aimed to describe population-level time trends in both prescribing patterns and short-term patient outcomes (HbA1c, weight, blood pressure, hypoglycemia and treatment discontinuation) after initiating new therapy. Materials and methods: We studied 81,532 UK patients with type 2 diabetes initiating a first to fourth line drug in primary care between 2010-2017 inclusive (Clinical Practice Research Datalink). Trends in new prescriptions and subsequent six and twelve-month adjusted changes in glycemic response (reduction in HbA1c), weight, blood pressure, and rates of hypoglycemia and treatment discontinuation were examined. Results: DPP4-inhibitor use second-line near doubled (41% of new prescriptions in 2017 vs. 22% 2010), replacing sulfonylureas as the most common second-line drug (29% 2017 vs. 53% 2010). SGLT2-inhibitors, introduced in 2013, comprised 17% of new first-fourth line prescriptions by 2017. First-line use of metformin remained stable (91% of new prescriptions in 2017 vs. 91% 2010). Over the study period there was little change in average glycemic response and treatment discontinuation. There was a modest reduction in weight second and third-line (second line 2017 vs. 2010: -1.5 kg (95%CI -1.9;-1.1), p<0.001), and a slight reduction in systolic blood pressure first to third-line (2017 vs. 2010 difference range -1.7 to -2.1 mmHg, all p<0.001). Hypoglycemia rates decreased second-line (incidence rate ratio 0.94 per-year (95%CI 0.88;1.00, p=0.04)), mirroring the decline in use of sulfonylureas. 4 Conclusions: Recent changes in prescribing of therapy in type 2 diabetes have not led to a change in glycemic response and have resulted in modest improvements in other population-level short-term patient outcomes.Medical Research Council (MRC)National Institute for Health Research (NIHR)Wellcome Trus

Predicting post one-year durability of glucose-lowering monotherapies in patients with newly-diagnosed type 2 diabetes mellitus – A MASTERMIND precision medicine approach (UKPDS 87)

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record Aims: Predicting likely durability of glucose-lowering therapies for people with type 2 diabetes (T2D) could help inform individualised therapeutic choices. Methods: We used data from UKPDS patients with newly-diagnosed T2D randomised to first-line glucose-lowering monotherapy with chlorpropamide–glibenclamide–basal insulin or metformin. In 2339 participants who achieved one-year HbA1c values <7.5% (<59 mmol/mol)–we assessed relationships between one-year characteristics and time to monotherapy-failure (HbA1c ≥ 7.5% or requiring second-line therapy). Model validation was performed using bootstrap sampling. Results: Follow-up was median (IQR) 11.0 (8.0–14.0) years. Monotherapy-failure occurred in 72%–82%–75% and 79% for those randomised to chlorpropamide–glibenclamide–basal insulin or metformin respectively–after median 4.5 (3.0–6.6)–3.7 (2.6–5.6)–4.2 (2.7–6.5) and 3.8 (2.6– 5.2) years. Time-to-monotherapy-failure was predicted primarily by HbA1c and BMI values–with other risk factors varying by type of monotherapy–with predictions to within ±2.5 years for 55%–60%–56% and 57% of the chlorpropamide–glibenclamide–basal insulin and metformin monotherapy cohorts respectively. Conclusions: Post one-year glycaemic durability can be predicted robustly in individuals with newly-diagnosed T2D who achieve HbA1c values < 7.5% one year after commencing traditional monotherapies. Such information could be used to help guide glycaemic management for individual patients.Medical Research Council (MRC

Development of models of care coordination for rare conditions: a qualitative study.

INTRODUCTION: Improving care coordination for people with rare conditions may help to reduce burden on patients and carers and improve the care that patients receive. We recently developed a taxonomy of different ways of coordinating care for rare conditions. It is not yet known which models of care coordination are appropriate in different situations. This study aimed to: (1) explore what types of care coordination may be appropriate in different situations, and (2) use these findings to develop hypothetical models of care coordination for rare conditions. METHODS: To explore appropriateness of different types of care coordination, we conducted interviews (n = 30), four focus groups (n = 22) and two workshops (n = 27) with patients, carers, healthcare professionals, commissioners, and charity representatives. Participants were asked about preferences, benefits and challenges, and the factors influencing coordination. Thematic analysis was used to develop hypothetical models of care coordination. Models were refined following feedback from workshop participants. RESULTS: Stakeholders prefer models of care that: are nationally centralised or a hybrid of national and local care, involve professionals collaborating to deliver care, have clear roles and responsibilities outlined (including administrative, coordinator, clinical and charity roles), provide access to records and offer flexible appointments (in terms of timing and mode). Many factors influenced coordination, including those relating to the patient (e.g., condition complexity, patient's location and ability to coordinate their own care), the healthcare professional (e.g., knowledge and time), the healthcare environment (e.g., resources) and societal factors (e.g., availability of funding). We developed and refined ten illustrative hypothetical models of care coordination for rare conditions. CONCLUSION: Findings underline that different models of care coordination may be appropriate in different situations. It is possible to develop models of care coordination which are tailored to the individual in context. Findings may be used to facilitate planning around which models of care coordination may be appropriate in different services or circumstances. Findings may also be used by key stakeholders (e.g. patient organisations, clinicians and service planners) as a decision-making tool