Rising Temperatures, Molting Phenology, and Epizootic Shell Disease in the American Lobster

Phenological mismatchmaladaptive changes in phenology resulting from altered timing of environmental cuesis an increasing concern in many ecological systems, yet its effects on disease are poorly characterized. American lobster (Homarus americanus) is declining at its southern geographic limit. Rising seawater temperatures are associated with seasonal outbreaks of epizootic shell disease (ESD), which peaks in prevalence in the fall. We used a 34-year mark-recapture data set to investigate relationships between temperature, molting phenology, and ESD in Long Island Sound, where temperatures are increasing at 0.4 degrees C per decade. Our analyses support the hypothesis that phenological mismatch is linked to the epidemiology of ESD. Warming spring temperatures are correlated with earlier spring molting. Lobsters lose diseased cuticle by molting, and early molting increases the intermolt period in the summer, when disease prevalence is increasing to a fall peak. In juvenile and adult male lobsters, September ESD prevalence was correlated with early molting, while October ESD prevalence was correlated with summer seawater temperature. This suggests that temperature-induced molting phenology affects the timing of the onset of ESD, but later in the summer this signal is swamped by the stronger signal of summer temperatures, which we hypothesize are associated with an increased rate of new infections. October ESD prevalence was approximate to 80% in years with hot summers and approximate to 30% in years with cooler summers. Yearly survival of diseased lobsters i

Prior event rate ratio adjustment produced estimates consistent with randomized trial: a diabetes case study

Objectives: Electronic health records (EHR) provide a valuable resource for assessing drug side-effects, but treatments are not randomly allocated in routine care creating the potential for bias. We conduct a case study using the Prior Event Rate Ratio (PERR) Pairwise method to reduce unmeasured confounding bias in side-effect estimates for two second-line therapies for type 2 diabetes, thiazolidinediones, and sulfonylureas. Study design and settings: Primary care data were extracted from the Clinical Practice Research Datalink (n = 41,871). We utilized outcomes from the period when patients took first-line metformin to adjust for unmeasured confounding. Estimates for known side-effects and a negative control outcome were compared with the A Diabetes Outcome Progression Trial (ADOPT) trial (n = 2,545). Results: When on metformin, patients later prescribed thiazolidinediones had greater risks of edema, HR 95% CI 1.38 (1.13, 1.68) and gastrointestinal side-effects (GI) 1.47 (1.28, 1.68), suggesting the presence of unmeasured confounding. Conventional Cox regression overestimated the risk of edema on thiazolidinediones and identified a false association with GI. The PERR Pairwise estimates were consistent with ADOPT: 1.43 (1.10, 1.83) vs. 1.39 (1.04, 1.86), respectively, for edema, and 0.91 (0.79, 1.05) vs. 0.94 (0.80, 1.10) for GI. Conclusion: The PERR Pairwise approach offers potential for enhancing postmarketing surveillance of side-effects from EHRs but requires careful consideration of assumptions.This article is freely available via Open Access. Click on the Publisher URL to access it via the publisher's site.The MASTERMIND (MRC APBI Stratification and Extreme Response Mechanism IN Diabetes) consortium is funded by the U.K Medical Research Council funded study grant number MR/N00633X/1. The funder had no role in study design, data collection, data analysis, data interpretation, or writing of the report. IQVIA provided some funding for this project.published version, accepted version (12 month embargo), submitted versio

Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults

This is the final version. Available from the publisher via the DOI in this record.The data that support the findings of this study are available from University of Exeter Medical School/Oxford University but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of University of Exeter Medical School/Oxford University. R code is made available in supplementary file (see Additional file 2).Background: There is much interest in the use of prognostic and diagnostic prediction models in all areas of clinical medicine. The use of machine learning to improve prognostic and diagnostic accuracy in this area has been increasing at the expense of classic statistical models. Previous studies have compared performance between these two approaches but their findings are inconsistent and many have limitations. We aimed to compare the discrimination and calibration of seven models built using logistic regression and optimised machine learning algorithms in a clinical setting, where the number of potential predictors is often limited, and externally validate the models. Methods: We trained models using logistic regression and six commonly used machine learning algorithms to predict if a patient diagnosed with diabetes has type 1 diabetes (versus type 2 diabetes). We used seven predictor variables (age, BMI, GADA islet-autoantibodies, sex, total cholesterol, HDL cholesterol and triglyceride) using a UK cohort of adult participants (aged 18–50 years) with clinically diagnosed diabetes recruited from primary and secondary care (n = 960, 14% with type 1 diabetes). Discrimination performance (ROC AUC), calibration and decision curve analysis of each approach was compared in a separate external validation dataset (n = 504, 21% with type 1 diabetes). Results: Average performance obtained in internal validation was similar in all models (ROC AUC ≥ 0.94). In external validation, there were very modest reductions in discrimination with AUC ROC remaining ≥ 0.93 for all methods. Logistic regression had the numerically highest value in external validation (ROC AUC 0.95). Logistic regression had good performance in terms of calibration and decision curve analysis. Neural network and gradient boosting machine had the best calibration performance. Both logistic regression and support vector machine had good decision curve analysis for clinical useful threshold probabilities. Conclusion: Logistic regression performed as well as optimised machine algorithms to classify patients with type 1 and type 2 diabetes. This study highlights the utility of comparing traditional regression modelling to machine learning, particularly when using a small number of well understood, strong predictor variables.National Institute for Health Research (NIHR

What to do with diabetes therapies when HbA1c lowering is inadequate:add, switch, or continue? A MASTERMIND study

This is the author accepted manuscript. The final version is available from BioMed Central via the DOI in this record.Background: It is unclear what to do when people with type 2 diabetes have had no or a limited glycemic response to a recently introduced medication. Intra-individual HbA1c variability can obscure true response. Some guidelines suggest stopping apparently ineffective therapy, but no studies have addressed this issue. Methods: In a retrospective cohort analysis using the UK Clinical Practice Research Datalink (CPRD), we assessed the outcome of 55,530 patients with type 2 diabetes starting their second or third non-insulin glucose lowering medication, with a baseline HbA1c >58mmol/mol (7.5%). For those with no HbA1c improvement or a limited response at 6 months (HbA1c fall <5.5mmol/mol [0.5%]) we compared HbA1c 12 months later in those who continued their treatment unchanged, switched to new treatment, or added new treatment. Results: An increase or a limited reduction in HbA1c was common, occurring in 21.9% (12,168/55,230), who had a mean HbA1c increase of 2.5mmol/mol (0.2%). After this limited response, continuing therapy was more frequent (n=9,308; 74%) than switching (n=1,177; 9%) or adding (n=2,163; 17%). Twelve months later, in those who switched medication HbA1c fell (-6.8mmol/mol [-0.6%], 95%CI -7.7, -6.0) only slightly more than those who continued unchanged (-5.1 mmol/mol [-0.5%], 95%CI -5.5, -4.8). Adding another new therapy was associated with a substantially better reduction (-12.4mmol/mol [-1.1%], 95%CI -13.1, -11.7). Propensity score matched subgroups demonstrated similar results. Conclusions: Where glucose lowering therapy does not appear effective on initial HbA1c testing, changing agents does not improve glycemic control. The initial agent should be continued with another therapy added.Medical Research Council (MRC)National Institute for Health Research (NIHR

Development of oedema is associated with an improved glycaemic response in patients initiating thiazolidinediones: a MASTERMIND study

Abstracts of the 51st EASD Annual Meeting, Stockholm, Sweden, 14–18 September 2015This is the author accepted manuscript. The final version is available from Springer VerlagBackground and aims: Oedema is a common and serious side effect of thiazolidinedione therapy. A stratified medicines approach would aim to give thiazolidinediones to patients likely to have a good glycaemic response but to not develop oedema. We investigated whether oedema was associated with glycaemic response to thiazolidinedione therapy. Materials and methods: We retrospectively studied 11,459 patients initiating a thiazolidinedione from UK primary care data (Clinical Practice Research Datalink), and identified medical records of new oedema in the subsequent twelve months. Response was defined as change in HbA1c at twelve months and was adjusted for baseline HbA1c, baseline BMI, gender and compliance (medication possession ratio). In secondary analyses we restricted oedema classification to patients with concomitant weight gain. As a comparison the same analysis was performed in 13,089 patients initiating a sulfonylurea. Results: The 5% of patients with recorded oedema on thiazolidinediones had a mean (CI) 2.2 (1.1-3.2)mmol/mol greater fall in HbA1c (p3 kg (p< 0.001) and a 3.6 (1.8-5.4)mmol/mol greater fall when weight gain >5 kg (p3 kg (p=0.19). Conclusion: Patients with Type 2 diabetes who develop oedema on initiating thiazolidinediones have an improved glycaemic response, and more severe oedema may be associated with greater reductions in HbA1c. An association between oedema and glycaemic response was not observed in patients initiating sulfonylureas. This supports glycaemic lowering and fluid retention being mediated by a common pathway of thiazolidinedione drug action.Supported by: MRC grant MR-K005707-

Patients who develop oedema on initiating thiazolidinedione therapy have an improved glycaemic response: a MASTERMIND study

Special Issue: Abstracts of the Diabetes UK Professional Conference 2015, ExCeL London, 11–13 March 2015This is the author accepted manuscript. The final version is available from WileyBackground/aim: Oedema is a common and serious side effect ofthiazolidinedione therapy. A stratified medicine approach wouldaim to give thiazolidinediones to patients likely to have a goodglycaemic response but not to develop oedema. We investigatedwhether oedema was associated with glycaemic response tothiazolidinedione therapy.Methods: We studied 10,486 patients initiating a thiazolidinedionefrom Clinical Practice Research Datalink (CPRD), and identifiedmedical records of oedema in the subsequent 12 months. Responsewas defined as change in HbA1c at 12 months and was adjusted forbaseline HbA1c, baseline body mass index, gender and adherence(medication possession ratio). In secondary analyses we restrictedoedema classification to patients with concomitant weight gain. As acomparison the same analysis was performed in 13,089 patientsinitiating a sulfonylurea.Results: The 3% of patients with recorded oedema onthiazolidinediones had a mean (confidence interval) 3 (1.7–4.3)mmol/mol greater fall in HbA1c (p 3kg (p 8kg (p 3kg (p=0.19).Conclusion: Patients with Type 2 diabetes who develop oedemaon initiating thiazolidinediones have an improved glycaemicresponse, and more severe oedema is associated with greaterHbA1c reduction. This supports glycaemic lowering andfluid retention being mediated by a common pathway ofthiazolidinedione drug action

Are the new drugs better? Changing UK prescribing of Type 2 diabetes medications and effects on HbA1c and weight, 2010 to 2016

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record.Aim: The availability of new glucose‐lowering drugs has changed UK National Institute of Clinical Excellence Type 2 diabetes guidelines, but there has been little evaluation of real‐world use of these drugs, or of the population‐level impact of their use. We examined changes in UK prescribing for patients starting second‐ and third‐line medications, and population‐level trends in glycaemic response and weight change. Methods: We extracted incident second‐ and third‐line oral prescription records for patients with Type 2 diabetes in the UK‐representative Clinical Practice Research Datalink, 2010 to 2016 (n = 68,902). Each year we calculated the proportion of each drug prescribed as the percentage of the total prescribed. We estimated annual mean six‐month HbA1c response and weight change using linear regression, standardised for clinical characteristics. Results: Use of Dipeptidyl peptidase‐4 (DPP4) inhibitors has increased markedly to overtake sulfonylureas as the most commonly prescribed second‐line drug in 2016 (43% vs 34% of total prescriptions compared with 18% v 59% in 2010). Use of sodium‐glucose co‐transporter‐2 (SGLT2) inhibitors has increased rapidly to 14% of second‐line and 27% of third‐line prescriptions in 2016. Mean HbA1c response at six months was stable over time (2016: 13.5 (95% confidence interval 12.8, 14.1) mmol/mol vs 2010: 13.9 (13.6;14.2) mmol/mol, p = 0.21). We found mean weight loss at six months in 2016, in contrast to 2010 where there was mean weight gain (2016: −1.2 (−0.9; −1.5) kg vs 2010: +0.4 (+0.3; +0.5) kg, p < 0.001). Conclusion: The pattern of drug prescribing to manage patients with Type 2 diabetes has changed rapidly in the United Kingdom. Increasing use of DPP4 inhibitors and SGLT2 inhibitors has not resulted in improved glycaemic control but has improved the body weight of patients starting second‐ and third‐line therapy. Acknowledgement: This abstract is submitted on behalf of the MASTERMIND consortium