Precision medicine in type 2 diabetes: Using individualised prediction models to optimise selection of treatment

This is the author accepted manuscript. The final version is available from the American Diabetes Association via the DOI in this recordDespite the known heterogeneity of type 2 diabetes and variable response to glucose lowering medications, current evidence on optimal treatment is predominantly based on average effects in clinical trials rather than individual-level characteristics. A precision medicine approach based on treatment response would aim to improve on this by identifying predictors of differential drug response for people based on their characteristics and then using this information to select optimal treatment. Recent research has demonstrated robust and clinically relevant differential drug response with all noninsulin treatments after metformin (sulfonylureas, thiazolidinediones, dipeptidyl peptidase 4 [DPP-4] inhibitors, glucagon-like peptide-1 [GLP-1] receptor agonists, and sodium–glucose cotransporter 2 [SGLT2] inhibitors) using routinely available clinical features. This Perspective reviews this current evidence and discusses how differences in drug response could inform selection of optimal type 2 diabetes treatment in the near future. It presents a novel framework for developing and testing precision medicine–based strategies to optimize treatment, harnessing existing routine clinical and trial data sources. This framework was recently applied to demonstrate that “subtype” approaches, in which people are classified into subgroups based on features reflecting underlying pathophysiology, are likely to have less clinical utility compared with approaches that combine the same features as continuous measures in probabilistic “individualized prediction” models.Research EnglandMedical Research Council (MRC

UK Breastfeeding Helpline support: An investigation of influences upon satisfaction

Background Incentive or reward schemes are becoming increasingly popular to motivate healthy lifestyle behaviours. In this paper, insights from a qualitative and descriptive study to investigate the uptake, impact and meanings of a breastfeeding incentive intervention integrated into an existing peer support programme (Star Buddies) are reported. The Star Buddies service employs breastfeeding peer supporters to support women across the ante-natal, intra-partum and post-partum period. Methods In a disadvantaged area of North West England, women initiating breastfeeding were recruited by peer supporters on the postnatal ward or soon after hospital discharge to participate in an 8 week incentive (gifts and vouchers) and breastfeeding peer supporter intervention. In-depth interviews were conducted with 26 women participants who engaged with the incentive intervention, and a focus group was held with the 4 community peer supporters who delivered the intervention. Descriptive analysis of routinely collected data for peer supporter contacts and breastfeeding outcomes before and after the incentive intervention triangulated and retrospectively provided the context for the qualitative thematic analysis. Results A global theme emerged of 'incentives as connectors', with two sub-themes of 'facilitating connections' and 'facilitating relationships and wellbeing'. The incentives were linked to discussion themes and gift giving facilitated peer supporter access for proactive weekly home visits to support women. Regular face to face contacts enabled meaningful relationships and new connections within and between the women, families, peer supporters and care providers to be formed and sustained. Participants in the incentive scheme received more home visits and total contact time with peer supporters compared to women before the incentive intervention. Full participation levels and breastfeeding rates at 6-8 weeks were similar for women before and after the incentive intervention. Conclusion The findings suggest that whilst the provision of incentives might not influence women's intentions or motivations to breastfeed, the connections forged provided psycho-social benefits for both programme users and peer supporters

Importance of food-demand management for climate mitigation

Recent studies show that current trends in yield improvement will not be sufficient to meet projected global food demand in 2050, and suggest that a further expansion of agricultural area will be required. However, agriculture is the main driver of losses of biodiversity and a major contributor to climate change and pollution, and so further expansion is undesirable. The usual proposed alternative - intensification with increased resource use - also has negative effects. It is therefore imperative to find ways to achieve global food security without expanding crop or pastureland and without increasing greenhouse gas emissions. Some authors have emphasised a role for sustainable intensification in closing global 'yield gaps' between the currently realised and potentially achievable yields. However, in this paper we use a transparent, data-driven model, to show that even if yield gaps are closed, the projected demand will drive further agricultural expansion. There are, however, options for reduction on the demand side that are rarely considered. In the second part of this paper we quantify the potential for demand-side mitigation options, and show that improved diets and decreases in food waste are essential to deliver emissions reductions, and to provide global food security in 2050.This work was funded by a grant to the University of Cambridge from BP as part of their Energy Sustainability Challenge.This is the accepted manuscript version. The final version is available from Nature Climate Change at http://www.nature.com/nclimate/journal/vaop/ncurrent/full/nclimate2353.html

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

Creaky knees: Is there a reason for concern? A qualitative study of the perspectives of people with knee crepitus

Objective: Crepitus is a feature of osteoarthritis that may affect one's participation in exercise. An informed understanding is required of the perceptions that people have of their knee crepitus and how it affects their exercise behaviours. This study aims to investigate the role that crepitus may play in beliefs about exercise and knee health. Methods: Focus group and individual interviews were conducted online with participants who had knee crepitus. The transcripts were thematically analysed through an inductive approach. Results: Five main themes were identified from 24 participants: (1) individual variation of, (2) occurrence of, (3) meaning of knee crepitus, (4) attitudes and exercise behaviours regarding crepitus, and (5) knowledge deficits and needs concerning crepitus during exercise. The variety of crepitus sounds described occurred with a range of exercises or after inactivity. For those already with osteoarthritis or other symptoms, crepitus was of less concern than symptoms such as pain. Most participants had not ceased exercise but may have modified movement due to crepitus and associated symptoms; some had increased intentional strength training to try alleviating it. Participants agreed that more understanding about the processes causing crepitus and what exercise was safe for knee health would be beneficial. Conclusion: Crepitus does not appear to be a major cause of concern for people who experience it. However, it is a factor that influences exercise behaviours as is pain. If health professionals could guide people with concerns about their crepitus, they may be more confident in exercising to benefit their joint health

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-

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

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