Precision Medicine in Type 2 Diabetes

Type 2 diabetes is a progressive disease characterised by raised blood glucose levels. Lowering of blood glucose is required to prevent symptoms of diabetes and to reduce the risk of people with type 2 diabetes developing diabetes-related complications. Metformin is the initial drug of choice to lower blood glucose for most people. However, for many people metformin eventually fails to control blood glucose and additional medication is required. At least four different types of glucose-lowering medication are recommended after metformin in current type 2 diabetes treatment guidelines. Choosing the best medication is left to the clinician and patient and is a major clinical dilemma. The degree of glucose-lowering appears to vary greatly between people for all the medication options. The same medication may appear to have a marked effect in one patient but little effect in another. Similarly, only some people develop side-effects. Despite this apparent variation it is largely unknown whether differences in treatment response and risk of side-effects can be predicted based on an individual patient’s characteristics. The aim of this thesis is to establish whether simple patient characteristics are associated with differences in treatment effect for common glucose-lowering medications. If they are, this could inform a precision medicine approach in type 2 diabetes, where medications are targeted to those people most likely to benefit

Experimental and finite element dynamic analysis of incrementally loaded reinforced concrete structures

This work investigates influence of damage in reinforced concrete (RC) structures on their dynamic properties through modal testing and non-linear finite element (FE) analysis. Five RC beams were designed with the fundamental flexural mode frequencies in the range of 6.5-18.0. Hz for the uncracked state. Mechanical properties of concrete, such as static and dynamic elastic moduli were determined from standard tests and ultra-sonic pulse velocity readings. The beams were incrementally loaded until the span/250 deflection limit was reached and their natural frequencies were measured from the free decay vibrations. The progressive damage reduced fundamental frequencies of tested beams by up to 25%. The non-linear FE analysis was carried out for RC beams and one two-span slab and the calculated reduced frequencies of the 1st and 2nd vibration modes were in excellent agreement with measurements. This led to the conclusion that, given that the non-linear analysis can capture degradation of dynamic stiffness due to cracking, the future dynamic performance and damage identification on the RC structure can be reliably determined from the same FE model. The results reveal potential of the combined modal testing and FE analysis to improve inspection and assessment of the in-service RC structures

Thermogravimetric data on the combustion of graphite and lignite

This is the raw data generated from a Mettler-Toledo TGA/DSC1, for the combustion of graphite powder and a German lignite char under isothermal conditions to investigate the production of CO and CO2 when combustion takes place under external mass transfer limitation

Onion leaf desiccation processes and implications for skin quality

Although onion skins play an important role in the life of an onion their development is poorly understood. The skins not only protect the onion bulb from disease and moisture loss but are fundamental to a customer’s perception of bulb quality. This study has increased the understanding of leaf desiccation and the formation of skins. A detailed phenological study across 32 commercial crops (‘Creamgold’) revealed that the scale of the 6th true leaf was the most common tissue to form the outermost entire skin on the onion bulb. A positive correlation was recorded between bulb diameter and the leaf number (where leaf 1 is the first true leaf) that formed the most-outer skin on the bulb. Skins that are forming but that are not yet completely dry are highly extensible which appears to enable them to withstand the rapid radial expansion of the bulb. Skin tensile strength was positively correlated with skin thickness and skin specific dry weight. Skin specific dry weight was higher in skins that developed from younger leaves, largely due to a higher number of cells in the cross sectional plane. It was posited that conditions that impair early leaf development, and limit the number of cells in the leaf, and therefore the amount of structural tissue in the scale, are responsible for skins that are easily torn and dislodged during handling operations. The importance on the timing of the events may explain the lack of consistency in findings associated with agronomic treatments and the variability in skin disorders between crops

Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared to models based on simple clinical features: an evaluation using clinical trial data

Background Recent research using data-driven cluster analysis has proposed five subgroups of diabetes with differences in diabetes progression and risk of complications. We aimed to compare the clinical utility of this subgroup-based approach for predicting patient outcomes with an alternative strategy of developing models for each outcome using simple patient characteristics. Methods We identified clusters in the ADOPT (n=4,351) trial cohort using the cluster analysis reported by Ahlqvist and colleagues (Lancet Diabetes Endocrinology 2018;6:361-69). Differences between clusters in glycaemic and renal progression were evaluated, and contrasted with stratification using simple continuous clinical features (respectively, age at diagnosis and baseline renal function). We tested the performance of a strategy of selecting glucose-lowering therapy using clusters with one combining simple clinical features (sex, BMI, age at diagnosis, baseline HbA1c) in an independent trial (RECORD (n=4,447)). Findings Clusters identified in trial data were similar to those described in the original study. Clusters showed differences in glycaemic progression, but a model with age at diagnosis alone explained a similar amount of variation in progression. We found differences in CKD incidence between clusters however baseline eGFR was a better predictor of time to CKD. Clusters differed in glycaemic response, with a particular benefit for cluster 3 (insulin-resistant) with thiazolidinediones and cluster 5 (older) with sulfonylureas. However simple clinical features outperformed clusters to select therapy for individual patients. Interpretation The proposed data-driven clusters differ in diabetes progression and treatment response, but models based on simple continuous clinical features are more useful to stratify patients. This suggests precision medicine in type 2 diabetes is likely to have most clinical utility if based on an approach of using specific phenotypic measures to predict specific outcomes, rather than assigning patients into subgroups

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

Objective: 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

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

Aim: 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

Development and validation of multivariable clinical diagnostic models to identify type 1 diabetes requiring rapid insulin therapy in adults aged 18 to 50 years

Objective: To develop and validate multivariable clinical diagnostic models to assist distinguishing between type 1 and type 2 diabetes in adults aged 18 to 50. Design: Multivariable logistic regression analysis was used to develop classification models integrating five pre-specified predictor variables, including clinical features (age of diagnosis, BMI) and clinical biomarkers (GADA and Islet Antigen 2 islet autoantibodies, Type 1 Diabetes Genetic Risk Score), to identify type 1 diabetes with rapid insulin requirement using data from existing cohorts. Setting: United Kingdom cohorts recruited from primary and secondary care. Participants: 1,352 (model development) and 582 (external validation) participants diagnosed with diabetes between the age of 18 and 50 years of white European origin. Main outcome measures: Type 1 diabetes was defined by rapid insulin requirement (within 3 years of diagnosis) and severe endogenous insulin deficiency (C-peptide <200pmol/L). Type 2 diabetes was defined by either a lack of rapid insulin requirement or, where insulin treated within 3 years, retained endogenous insulin secretion (C-peptide >600pmol/L at ≥5 years diabetes duration). Model performance was assessed using area under the receiver operating characteristic curve (ROC AUC), and internal and external validation. 4 Results: Type 1 diabetes was present in 13% of participants in the development cohort. All five predictor variables were discriminative and independent predictors of type 1 diabetes (p<0.001 for all) with individual ROC AUC ranging from 0.82 to 0.85. Model performance was high: ROC AUC range 0.90 [95%CI 0.88, 0.93] (clinical features only) to 0.97 [0.96, 0.98] (all predictors) with low prediction error. Results were consistent in external validation (clinical features and GADA ROC AUC 0.93 [0.90, 0.96]). Conclusions: Clinical diagnostic models integrating clinical features with biomarkers have high accuracy for identifying type 1 diabetes with rapid insulin requirement, and could assist clinicians and researchers in accurately identifying patients with type 1 diabetes

Precision medicine in Type 2 diabetes: Clinical markers of insulin resistance are associated with altered short and long-term glycemic response to DPP4-inhibitor therapy

Objective A ‘precision’ approach to type 2 diabetes therapy would aim to target treatment according to patient characteristics. We examined if measures of insulin resistance and secretion were associated with glycemic response to DPP4-inhibitor therapy. Research Design and Methods We evaluated whether markers of insulin resistance and insulin secretion were associated with 6 month glycemic response in a prospective study of non-insulin treated participants starting DPP4-inhibitor therapy (PRIBA, n=254), with replication for routinely available markers in UK electronic healthcare records (CPRD, n=23,001). In CPRD we evaluated associations between baseline markers and 3 year durability of response. To test the specificity of findings we repeated analyses for GLP-1 receptor agonists (PRIBA n=339, CPRD n=4,464). Results In PRIBA markers of higher insulin resistance (higher fasting C-peptide (p=0.03), HOMA2 insulin resistance (p=0.01) and triglycerides (p<0.01)) were associated with reduced 6 month HbA1c response to DPP4 inhibitors. In CPRD higher triglycerides and BMI were associated with reduced HbA1c response (both p<0.01). A subgroup defined by obesity (BMI≥30kg/m2) and high triglycerides (≥2.3mmol/L) had reduced 6 month response in both datasets (PRIBA HbA1c reduction 5.3[95%CI 1.8,8.6]mmol/mol (0.5%) (obese, high triglycerides) vs 11.3[8.4,14.1] mmol/mol (1.0%) (non-obese, normal triglycerides), p=0.01. In CPRD the obese, high triglycerides subgroup also had less durable response (hazard ratio 1.28[1.16,1.41], p<0.001). There was no association between markers of insulin resistance and response to GLP-1 receptor agonists. Conclusions Markers of higher insulin resistance are consistently associated with reduced glycemic response to DPP4-inhibitors. This finding provides a starting point for the application of a precision diabetes approach to DPP4-inhibitor therapy

Sex and BMI alter the benefits and risks of sulfonylureas and thiazolidinediones in type 2 diabetes: A framework for evaluating stratification using routine clinical and individual trial data

OBJECTIVE The choice of therapy for type 2 diabetes after metformin is guided by overall estimates of glycemic response and side-effects seen in large cohorts. A stratified approach to therapy would aim to improve on this by identifying subgroups of patients whose glycaemic response or risk of side-effects differ markedly. We assessed if simple clinical characteristics could identify patients with differing glycemic response and side-effects with sulfonylureas and thiazolidinediones. RESEARCH DESIGN AND METHODS We studied 22,379 patients starting sulfonylurea or thiazolidinedione therapy in U.K. Clinical Practice Research Datalink (CPRD) to identify features associated with increased one-year HbA1c fall with one therapy class and reduced with the second. We then assessed if pre-specified patient subgroups defined by the differential clinical factors showed differing five-year glycemic response and side-effects with sulfonylureas and thiazolidinediones using individual randomised trial data from ADOPT (first-line therapy, n=2,725) and RECORD (second-line therapy, n=2,222). Further replication was conducted using routine clinical data from the GoDARTS (n=1,977). RESULTS In CPRD male sex and lower BMI were associated with greater glycemic response with sulfonylureas and a lesser response with thiazolidinediones (both p<0.001). In ADOPT and RECORD non-obese males had a greater overall HbA1c reduction with sulfonylureas than thiazolidinediones (p<0.001); in contrast obese females had a greater HbA1c reduction with thiazolidinediones than sulfonylureas (p<0.001). Weight gain and oedema risk with thiazolidinediones were greatest in obese females however hypoglycaemia risk with sulfonylureas was similar across all subgroups. CONCLUSIONS Patient subgroups defined by sex and BMI have a different pattern of benefits and risks on thiazolidinedione and sulfonylurea therapy. Subgroup specific estimates can inform discussion about the choice of therapy after metformin for an individual patient. Our approach using routine and shared trial data provides a framework for future stratification research in type 2 diabetes