AbstractObjectiveTo compare individualized treatment selection strategies based on predicted individual-level treatment effects from a causal forest machine learning algorithm and a penalized regression model.Study Design and SettingCohort study characterizing individual-level glucose-lowering response (6 month reduction in HbA1c) in people with type 2 diabetes initiating SGLT2-inhibitor or DPP4-inhibitor therapy. Model development set comprised 1,428 participants in the CANTATA-D and CANTATA-D2 trials (SGLT2-inhibitor versus DPP4-inhibitor). For external validation, calibration of observed versus predicted differences in HbA1c in patient strata defined by size of predicted HbA1c benefit was evaluated in 18,741 UK primary care patients (Clinical Practice Research Datalink).ResultsHeterogeneity in treatment effects was detected in trial participants with both approaches (causal forest: 98.6% & penalized regression: 81.7% predicted to have a benefit on SGLT2-inhibitor therapy over DPP4-inhibitor therapy). In validation, calibration was good with penalized regression but sub-optimal with causal forest. A strata with an HbA1c benefit >10 mmol/mol with SGLT2-inhibitors (3.7% of patients, observed benefit 11.0 mmol/mol [95%CI 8.0-14.0]) was identified using penalized regression but not causal forest, and a much larger strata with an HbA1c benefit 5-10 mmol with SGLT2-inhibitors was identified with penalized regression (regression: 20.9% of patients, observed benefit 7.8 mmol/mol (95%CI 6.7-8.9); causal forest 11.6%, observed benefit 8.7 mmol/mol (95%CI 7.4-10.1).ConclusionWhen evaluating treatment effect heterogeneity researchers should not rely on causal forest (or other similar machine learning algorithms) alone, and must compare outputs with standard regression.What is new?QuestionWhat is the comparative utility of machine learning compared to standard regression for identifying variation in patient-level outcomes (treatment effect heterogeneity) due to different treatments?FindingsCausal forest and penalized regression models were developed using trial data to predict glycated hemoglobin [HbA1c]) outcomes with SGLT2-inhibitor and DPP4-inhibitor therapy in 1,428 individuals with type 2 diabetes. In external validation (18,741 patients), penalized regression outperformed causal forest at identifying population strata with a superior glycemic response to SGLT2-inhibitors compared to DPP4-inhibitors.ImplicationsStudies estimating treatment effect heterogeneity should not solely rely on machine learning and should compare results with standard regression.

ABSTRACTBackgroundNon-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in type 2 diabetes (T2D) and beyond. Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and ultimately hepatocellular carcinomas.Methods and FindingsUtilizing the baseline data from the IMI DIRECT participants (n=1514) we sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. Multi-omic (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, and measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI image-derived liver fat content (<5% or ≥5%). We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and Random Forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operator characteristic area under the curve (ROCAUC) of 0.84 (95% confidence interval (CI)=0.82, 0.86), which compared with a ROCAUC of 0.82 (95% CI=0.81, 0.83) for a model including nine clinically-accessible variables. The IMI DIRECT prediction models out-performed existing non-invasive NAFLD prediction tools.ConclusionsWe have developed clinically useful liver fat prediction models (see:www.predictliverfat.org) and identified biological features that appear to affect liver fat accumulation.

Abstract
. Precision medicine aims to target treatment to an individual based on their clinical features. A differential drug response, critical to using these features for therapy selection, has never been examined directly in type 2 diabetes. We tested two specific hypotheses: 1) individuals with BMI > 30kg/m2, compared with BMI ≤ 30kg/m2, have greater glucose lowering with thiazolidinediones than DPP4-inhibitors, and 2) individuals with eGFR 60-90mls/min/1.73m2 compared with eGFR > 90mls/min/1.73m2 have greater glucose lowering with DPP4-inhibitors than SGLT2-inhibitors. The primary endpoint for both hypotheses was the achieved HbA1c difference between strata for the two drugs. We conducted a UK based randomised, double-blind, three-way crossover trial of 16 weeks treatment with each of sitagliptin 100mg/day, canagliflozin 100mg/day and pioglitazone 30mg/day added to metformin alone or metformin plus sulfonylurea. Overall, the achieved HbA1c was similar for the three drugs. Participants with BMI > 30kg/m2, compared with BMI ≤ 30kg/m2, had a 2.88 mmol/mol (95% CI 0.98,4.79) lower HbA1c on pioglitazone than on sitagliptin (n = 356, P = 0.003). Participants with eGFR 60-90mls/min/1.73m2, compared with eGFR > 90mls/min/1.73m2, had a 2.90 mmol/mol (95% CI 1.19,4.61) lower HbA1c on sitagliptin than on canagliflozin (n = 342, P = 0.001). In this first precision medicine trial in type 2 diabetes, our findings support the use of simple routinely available measures to identify the drug likely to deliver the greatest glycaemic reduction.

AbstractLinear mixed effects models are frequently used in biomedical statistics to model the trajectory of a repeatedly measured longitudinal variable, such as a biomarker, over time. However, population-level estimates may be biased by censoring bias resulting from exit criteria that depend on the variable in question. A joint longitudinal-survival model, in which the exit criteria and longitudinal variable are modelled simultaneously, may address this bias. Using blood glucose progression (change in HbA1c) in type 2 diabetes patients on metformin monotherapy as an example, we study the potential benefit of using joint models to model trajectory of a biomarker in observational data. 7,712 patients with type 2 diabetes initiating metformin monotherapy were identified in UK Biobank’s general practice (GP) linked records. Genetic information was extracted from UK Biobank, and prescription records, baseline clinical features and biomarkers, and longitudinal HbA1c measures were extracted from GP records. Exit criteria for follow-up for a patient was defined as progression to an additional glucose-lowering drug (which is more likely in patient with higher HbA1c). Estimates of HbA1c trajectory over time were compared using linear mixed effect model approaches (which do not account for censoring bias) and joint models. In the primary analysis, a 0.19 mmol/mol per year higher (p = 0.01) HbA1c gradient was estimated using the joint model compared to the linear mixed effects model. This difference between models was attenuated (0.13 mmol/mol per year higher, p=0.43) when baseline clinical features and biomarkers were included as additional covariates.Censoring bias should be carefully considered when modelling trajectories of repeatedly measured longitudinal variables in observational data. Joint longitudinal-survival models are a useful approach to identify and potentially correct for censoring bias when estimating population-level trajectories.Author SummaryModelling biomarkers that change over time in real world data is a challenging statistical problem due to many potential sources of bias. For example, when studying a chronic disease using a biomarker or other measurement that represents disease severity, medication intended to affect that measurement has a profound effect on how it will change over time. One common way to control for this is to study a cohort on the same treatment strategy. That way, results are not influenced by treatment change. If a patient progresses to stronger medication, then future data is no longer used. However, this approach introduces its own bias. Patients whose condition progress particularly quickly are more likely to change treatment more rapidly (and therefore be removed from further analysis, or ‘censored’), so the cohort is biased towards those whose condition progresses slower. In this paper we apply a technique called joint longitudinal-survival modelling which can adjust for this censoring bias and produce less biased estimates of progression rates. We use HbA1c (a widely used measure of glucose control) in type 2 diabetes as an example, however our methods are theoretically applicable to a range of problems across medicine in which a biomarker or feature is repeatedly measured in an individual.

. OBJECTIVE
. To determine whether presentation, progression, and genetic susceptibility of robustly defined adult-onset type 1 diabetes (T1D) are altered by diagnosis age.
.
.
. RESEARCH DESIGN AND METHODS
. We compared the relationship between diagnosis age and presentation, C-peptide loss (annual change in urine C-peptide–creatinine ratio [UCPCR]), and genetic susceptibility (T1D genetic risk score [GRS]) in adults with confirmed T1D in the prospective StartRight study, 1,798 adults with new-onset diabetes. T1D was defined in two ways: two or more positive islet autoantibodies (of GAD antibody, IA-2 antigen, and ZnT8 autoantibody) irrespective of clinical diagnosis (n = 385) or one positive islet autoantibody and a clinical diagnosis of T1D (n = 180).
.
.
. RESULTS
. In continuous analysis, age of diagnosis was not associated with C-peptide loss for either definition of T1D (P > 0.1), with mean (95% CI) annual C-peptide loss in those diagnosed before and after 35 years of age (median age of T1D defined by two or more positive autoantibodies): 39 (31–46) vs. 44% (38–50) with two or more positive islet autoantibodies and 43 (33–51) vs. 39% (31–46) with clinician diagnosis confirmed by one positive islet autoantibody (P > 0.1). Baseline C-peptide and T1D GRS were unaffected by age of diagnosis or T1D definition (P > 0.1). In T1D defined by two or more autoantibodies, presentation severity was similar in those diagnosed before and after 35 years of age: unintentional weight loss, 80 (95% CI 74–85) vs. 82% (76–87); ketoacidosis, 24 (18–30) vs. 19% (14–25); and presentation glucose, 21 (19–22) vs. 21 mmol/L (20–22) (all P ≥ 0.1). Despite similar presentation, older adults were less likely to be diagnosed with T1D, insulin-treated, or admitted to hospital.
.
.
. CONCLUSIONS
. When adult-onset T1D is robustly defined, the presentation characteristics, progression, and T1D genetic susceptibility are not altered by age of diagnosis.
.

AbstractThe application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug–omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug–drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.

OBJECTIVES: We aimed to compare the performance of approaches for classifying insulin-treated diabetes within research datasets without measured classification biomarkers, evaluated against two independent biological definitions of diabetes type. STUDY DESIGN AND SETTING: We compared accuracy of ten reported approaches for classifying insulin-treated diabetes into type 1 (T1D) and type 2 (T2D) diabetes in two cohorts: UK Biobank (UKBB) n = 26,399 and Diabetes Alliance for Research in England (DARE) n = 1,296. The overall performance for classifying T1D and T2D was assessed using: a T1D genetic risk score and genetic stratification method (UKBB); C-peptide measured at >3 years diabetes duration (DARE). RESULTS: Approaches' accuracy ranged from 71% to 88% (UKBB) and 68% to 88% (DARE). When classifying all participants, combining early insulin requirement with a T1D probability model (incorporating diagnosis age and body image issue [BMI]), and interview-reported diabetes type (UKBB available in only 15%) consistently achieved high accuracy (UKBB 87% and 87% and DARE 85% and 88%, respectively). For identifying T1D with minimal misclassification, models with high thresholds or young diagnosis age (

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.

Monitoring glycaemic control in type 2 diabetes is essential to allow appropriate titration of medication and prevent diabetes complications. In developed countries glucose control is monitored mainly by glycated haemoglobin (HbA1c) testing or intensive home capillary glucose measurements. HbA1c is not used routinely in low resource settings because of cost, and a number of conditions that are relatively common in this population may result in HbA1c results that poorly reflect blood glucose levels; for example sickle cell and other haemo-globinopathies; anaemia, malaria, renal disease. The alternative measures to HbA1c recommended for monitoring (i.e. fructosamine and glycated albumin, or single glucose measures) in situations where HbA1c may be unreliable have not been well studied in African populations. Current clinical practice in such settings varies, with a single fasting glucose measure used by many clinicians to inform treatment titration, but others routinely use non fasting ‘random’ measurements.

A key question for use of fasting glucose in monitoring glycaemic burden is whether it is significantly affected by exercise (prolonged walking to the clinic). This is because majority of the patients in Uganda and other low resource settings (e.g. SSA countries) walk long distances to the diabetes clinics and fasting/non-fasting blood glucose will often be measured after an abnormally prolonged fast (of more than the recommended 8 hours) and/or very long walk to the clinic.

On the other hand, a key barrier to therapy intensification in the management of type 2 diabetes is fear of hypoglycaemia. As intensive glucose monitoring is not possible in low resource settings, often sulphonylurea and insulin glucose lowering therapy (two of the 3 therapy classes widely available) are only started and maintained at glycaemic thresholds far higher than recommended elsewhere (therapeutic inertia) because of the fear of hypoglycaemia. It is not clear whether this fear of hypoglycaemia is justified. Little is known about hypoglycaemia in the patients receiving these treatments in sub-Saharan Africa, with only a small number of retrospective studies that have not used objective measurements.

The aim of the thesis is to determine the optimal method for monitoring glycaemic burden and impact of exercise on fasting glucose and to understand the rates and determinants of hypoglycaemia with sulphonylurea and insulin treatment.

In Chapter 1, we review the current literature for monitoring glycaemic control in the clinical management of diabetes

In Chapter 2 we compare the performance of three tests (HbA1c, fasting and non-fasting/random glucose) commonly used for monitoring glycaemic burden in type 2 diabetes patients. We show that HbA1c is the overall best measure of glycaemic burden, despite high prevalence of other medical conditions that may affect its accuracy (e.g. anaemia, haemoglobinopathies). We also demonstrate that fasting plasma glucose and random plasma glucose strongly correlate with CGM glucose and HbA1c, have reasonable sensitivity and specificity to detect poor glycaemic control and the difference in performance between these tests is modest.

In Chapter 3 we assess the performance of glycated albumin and fructosamine against continuous glucose monitoring in comparison to other measures as an assessment of glucose burden in participants with type 2 diabetes and determine whether a recently developed automated glycated albumin assay can improve performance over and above fructosamine.

In chapter 4 we assess the impact of prolonged walking on fasting glucose in type 2 diabetes patients. We demonstrate that fasting plasma glucose is not significantly affected by walking to the clinic.

In Chapter 5 we assess the rates and determinants of continuous glucose monitoring measured hypoglycaemia in patients receiving insulin or sulfonylurea treatment in the Ugandan population, in comparison to those receiving metformin or diet treatment. We show that in a low-resource sub-Saharan African setting, hypoglycaemia is infrequent among people with type 2 diabetes receiving sulphonylurea treatment, and the modest excess occurs predominantly in those with tight glycaemic control.

INTRODUCTION: People living with diabetes in low-resource settings may be at increased hypoglycemia risk due to food insecurity and limited access to glucose monitoring. We aimed to assess hypoglycemia risk associated with sulphonylurea (SU) and insulin therapy in people living with type 2 diabetes in a low-resource sub-Saharan African setting. RESEARCH DESIGN AND METHODS: This study was conducted in the outpatients' diabetes clinics of two hospitals (one rural and one urban) in Uganda. We used blinded continuous glucose monitoring (CGM) and self-report to compare hypoglycemia rates and duration in 179 type 2 diabetes patients treated with sulphonylureas (n=100) and insulin (n=51) in comparison with those treated with metformin only (n=28). CGM-assessed hypoglycemia was defined as minutes per week below 3mmol/L (54mg/dL) and number of hypoglycemic events below 3.0 mmol/L (54 mg/dL) for at least 15 minutes. RESULTS: CGM recorded hypoglycemia was infrequent in SU-treated participants and did not differ from metformin: median minutes/week of glucose

BACKGROUND: Current treatment guidelines do not provide recommendations to support the selection of treatment for most people with type 2 diabetes. We aimed to develop and validate an algorithm to allow selection of optimal treatment based on glycaemic response, weight change, and tolerability outcomes when choosing between SGLT2 inhibitor or DPP-4 inhibitor therapies. METHODS: in this retrospective cohort study, we identified patients initiating SGLT2 and DPP-4 inhibitor therapies after Jan 1, 2013, from the UK Clinical Practice Research Datalink (CPRD). We excluded those who received SGLT2 or DPP-4 inhibitors as first-line treatment or insulin at the same time, had estimated glomerular filtration rate (eGFR) of less than 45 mL/min per 1·73 m2, or did not have a valid baseline glycated haemoglobin (HbA1c) measure (

Abstract
. Background
. It is unclear whether diet affects glycaemic control in type 2 diabetes (T2D), over and above its effects on bodyweight. We aimed to assess whether changes in dietary patterns altered glycaemic control independently of effects on bodyweight in newly diagnosed T2D.
.
. Methods
. We used data from 4-day food diaries, HbA1c and potential confounders in participants of the Early-ACTivity-In-Diabetes trial measured at 0, 6 and 12 months. At baseline, a ‘carb/fat balance’ dietary pattern and an ‘obesogenic’ dietary pattern were derived using reduced-rank regression, based on hypothesised nutrient-mediated mechanisms linking dietary intake to glycaemia directly or via obesity. Relationships between 0 and 6 month change in dietary pattern scores and baseline-adjusted HbA1c at 6 months (n = 242; primary outcome) were assessed using multivariable linear regression. Models were repeated for periods 6–12 months and 0–12 months (n = 194 and n = 214 respectively; secondary outcomes).
.
. Results
. Reductions over 0–6 months were observed in mean bodyweight (− 2.3 (95% CI: − 2.7, − 1.8) kg), body mass index (− 0.8 (− 0.9, − 0.6) kg/m2), energy intake (− 788 (− 953, − 624) kJ/day), and HbA1c (− 1.6 (− 2.6, -0.6) mmol/mol). Weight loss strongly associated with lower HbA1c at 0–6 months (β = − 0.70 [95% CI − 0.95, − 0.45] mmol/mol/kg lost). Average fat and carbohydrate intakes changed to be more in-line with UK healthy eating guidelines between 0 and 6 months. Dietary patterns shifting carbohydrate intakes higher and fat intakes lower were characterised by greater consumption of fresh fruit, low-fat milk and boiled/baked potatoes and eating less of higher-fat processed meats, butter/animal fats and red meat. Increases in standardised ‘carb/fat balance’ dietary pattern score associated with improvements in HbA1c at 6 months independent of weight loss (β = − 1.54 [− 2.96, − 0.13] mmol/mol/SD). No evidence of association with HbA1c was found for this dietary pattern at other time-periods. Decreases in ‘obesogenic’ dietary pattern score were associated with weight loss (β = − 0.77 [− 1.31, − 0.23] kg/SD) but not independently with HbA1c during any period.
.
. Conclusions
. Promoting weight loss should remain the primary nutritional strategy for improving glycaemic control in early T2D. However, improving dietary patterns to bring carbohydrate and fat intakes closer to UK guidelines may provide small, additional improvements in glycaemic control.
.
. Trial registration
. ISRCTN92162869. Retrospectively registered on 25 July 2005
.

CONTEXT: the importance of the autoantibody level at diagnosis of type 1 diabetes (T1D) is not clear. OBJECTIVE: We aimed to assess the association of glutamate decarboxylase (GADA), islet antigen-2 (IA-2A), and zinc transporter 8 (ZnT8A) autoantibody levels with clinical and genetic characteristics at diagnosis of T1D. METHODS: We conducted a prospective, cross-sectional study. GADA, IA-2A, and ZnT8A were measured in 1644 individuals with T1D at diagnosis using radiobinding assays. Associations between autoantibody levels and the clinical and genetic characteristics for individuals were assessed in those positive for these autoantibodies. We performed replication in an independent cohort of 449 people with T1D. RESULTS: GADA and IA-2A levels exhibited a bimodal distribution at diagnosis. High GADA level was associated with older age at diagnosis (median 27 years vs 19 years, P = 9 × 10-17), female sex (52% vs 37%, P = 1 × 10-8), other autoimmune diseases (13% vs 6%, P = 3 × 10-6), and HLA-DR3-DQ2 (58% vs 51%, P =. 006). High IA-2A level was associated with younger age of diagnosis (median 17 years vs 23 years, P = 3 × 10-7), HLA-DR4-DQ8 (66% vs 50%, P = 1 × 10-6), and ZnT8A positivity (77% vs 52%, P = 1 × 10-15). We replicated our findings in an independent cohort of 449 people with T1D where autoantibodies were measured using enzyme-linked immunosorbent assays. CONCLUSION: Islet autoantibody levels provide additional information over positivity in T1D at diagnosis. Bimodality of GADA and IA-2A autoantibody levels highlights the novel aspect of heterogeneity of T1D. This may have implications for T1D prediction, treatment, and pathogenesis.

Aims
This study aimed to investigate the frequency of islet autoantibody positivity in adult patients with recently diagnosed diabetes in Uganda and its associated characteristics.


Methods
Autoantibodies to glutamic acid decarboxylase-65 (GADA), zinc transporter 8 (ZnT8-A), and tyrosine phosphatase (IA-2A) were measured in 534 adult patients with recently diagnosed diabetes. Islet autoantibody positivity was defined based on diagnostic thresholds derived from a local adult population without diabetes. The socio-demographic, clinical, and metabolic characteristics of islet autoantibody-positive and negative participants were then compared. The differences in these characteristics were analysed using the x2 test for categorical data and the Kruskal Wallis test for continuous data. Multivariate analysis was performed to identify predictors of islet autoantibody positivity.


Results
Thirty four (6.4%) participants were positive for ≥1 islet autoantibody. GADA, IA-2A and ZnT8-A positivity was detected in 17 (3.2%), 10 (1.9%), and 7 (1.3%) participants, respectively. Compared with those negative for islet autoantibodies, participants positive for islet autoantibodies were more likely to live in a rural area (n = 18, 52.9% Vs n = 127, 25.5%, p = 0.005), to be initiated on insulin therapy (n = 19, 55.9% Vs n = 134, 26.8%, p<0.001), to have a lower median waist circumference (90 [80–99] cm Vs 96 [87–104.8], p = 0.04), waist circumference: height ratio (0.55 [0.50–0.63] vs 0.59 [0.53–0.65], p = 0.03), and fasting C-peptide concentration (0.9 [0.6–1.8] Vs 1.4 [0.8–2.1] ng/ml, p = 0.01). On multivariate analysis, living in a rural area (odds ratio or OR 3.62, 95%CI 1.68–7.80, p = 0.001) and being initiated on insulin therapy (OR 3.61, 95% CI 1.67–7.83, p = 0.001) were associated with islet autoantibody positivity.


Conclusion
The prevalence of islet autoantibody positivity was relatively low, suggesting that pancreatic autoimmunity is a rare cause of new-onset diabetes in this adult Ugandan population. Living in a rural area and being initiated on insulin therapy were independently associated with islet autoantibody positivity in this study population.

OBJECTIVE: Recent joint American Diabetes Association and European Association for the Study of Diabetes guidelines recommend routine islet autoantibody testing in all adults newly diagnosed with type 1 diabetes. We aimed to assess the impact of routine islet autoantibody testing in this population. RESEARCH DESIGN AND METHODS: We prospectively assessed the relationship between islet autoantibody status (GADA, IA-2A, and ZNT8A), clinical and genetic characteristics, and progression (annual change in urine C-peptide-to-creatinine ratio [UCPCR]) in 722 adults (≥18 years old at diagnosis) with clinically diagnosed type 1 diabetes and diabetes duration

Abstract
. Aims/hypothesis
. Screening programmes can detect cases of undiagnosed diabetes earlier than symptomatic or incidental diagnosis. However, the improvement in time to diagnosis achieved by screening programmes compared with routine clinical care is unclear. We aimed to use the UK Biobank population-based study to provide the first population-based estimate of the reduction in time to diabetes diagnosis that could be achieved by HbA1c-based screening in middle-aged adults.
.
. Methods
. We studied UK Biobank participants aged 40–70 years with HbA1c measured at enrolment (but not fed back to participants/clinicians) and linked primary and secondary healthcare data (n=179,923) and identified those with a pre-existing diabetes diagnosis (n=13,077, 7.3%). Among the remaining participants (n=166,846) without a diabetes diagnosis, we used an elevated enrolment HbA1c level (≥48 mmol/mol [≥6.5%]) to identify those with undiagnosed diabetes. For this group, we used Kaplan–Meier analysis to assess the time between enrolment HbA1c measurement and subsequent clinical diabetes diagnosis up to 10 years, and Cox regression to identify clinical factors associated with delayed diabetes diagnosis.
.
. Results
. In total, 1.0% (1703/166,846) of participants without a diabetes diagnosis had undiagnosed diabetes based on calibrated HbA1c levels at UK Biobank enrolment, with a median HbA1c level of 51.3 mmol/mol (IQR 49.1–57.2) (6.8% [6.6–7.4]). These participants represented an additional 13.0% of diabetes cases in the study population relative to the 13,077 participants with a diabetes diagnosis. The median time to clinical diagnosis for those with undiagnosed diabetes was 2.2 years, with a median HbA1c at clinical diagnosis of 58.2 mmol/mol (IQR 51.0–80.0) (7.5% [6.8–9.5]). Female participants with lower HbA1c and BMI measurements at enrolment experienced the longest delay to clinical diagnosis.
.
. Conclusions/interpretation
. Our population-based study shows that HbA1c screening in adults aged 40–70 years can reduce the time to diabetes diagnosis by a median of 2.2 years compared with routine clinical care. The findings support the use of HbA1c screening to reduce the time for which individuals are living with undiagnosed diabetes.
.
. Graphical abstract
.
.

Type 1 diabetes is a chronic autoimmune disease characterised by severe endogenous insulin deficiency resulting from the progressive destruction of insulin-secreting beta cells of the pancreatic islets. The detection of one or more circulating islet autoantibodies is fundamental in confirming the diagnosis of the condition.
Type 1 diabetes has been poorly characterised in Africa, especially in sub-Saharan Africa, due to the lack of robust observational data. Previous studies conducted amongst young people in sub-Saharan Africa have reported an atypical phenotype suggesting a non-autoimmune aetiology. Whether this is true is uncertain as many of these previous studies were limited by small sample sizes and the use of non-standardised methods. Examining the phenotype and aetiology of type 1 diabetes in African populations will be fundamental in providing insights into the progression of the disease and strategies for prevention and treatment. Furthermore, type 1 diabetes is historically associated with high mortality in the African continent. The mortality is thought to be highest amongst young children and those close to diabetes diagnosis. Whether high mortality introduces a survival bias with the possible enrichment of a particular phenotype of diabetes has not been previously studied.
The aim of this thesis is to examine the phenotype and aetiology of diabetes amongst young people with a clinician diagnosis of type 1 diabetes in sub-Saharan Africa. The thesis also seeks to investigate the association between the phenotype of diabetes and survival in sub-Saharan Africa.

In Chapter 1, we present an overview of type 1 diabetes and review current evidence on the phenotype of type 1 diabetes in sub-Saharan Africa. We also describe the datasets and clinical studies used in the subsequent chapters of this thesis.
In Chapter 2, we describe the clinical utility of the urinary C-peptide to creatinine ratio in assessing endogenous insulin secretion in patients with diabetes in Cameroon and demonstrate that the use of home meal stimulation may not be appropriate in this setting.
In Chapter 3, we describe the performance of home-collected dried blood spot C-peptide measurements in assessing endogenous insulin secretion in people with young-onset diabetes in Cameroon. We show that simple dried blood spot C-peptide measurements provide a robust method of assessing endogenous insulin secretion in this population.
In Chapter 4, we examine the phenotype and markers of type 1 diabetes aetiology in people with a clinician diagnosis of type 1 diabetes in Cameroon. We show that most young people diagnosed with type 1 diabetes likely have atypical non-autoimmune diabetes and do not have features suggestive of autoimmune type 1 or type 2 diabetes.
In Chapter 5, we examine the mortality rate within a group of children and adolescents with a clinician diagnosis of type 1 diabetes in Cameroon. We demonstrate that mortality was substantially high and mainly associated with place of care and education.
Chapter 6 presents an overview of the main findings, conclusions, limitations, and perspectives for future research generated by this thesis.

Abstract
. Aims/hypothesis
. Apparent type 2 diabetes is increasingly reported in lean adult individuals in sub-Saharan Africa. However, studies undertaking robust clinical and metabolic characterisation of lean individuals with new-onset type 2 diabetes are limited in this population. This cross-sectional study aimed to perform a detailed clinical and metabolic characterisation of newly diagnosed adult patients with diabetes in Uganda, in order to compare features between lean and non-lean individuals.
.
. Methods
. Socio-demographic, clinical, biophysical and metabolic (including oral glucose tolerance test) data were collected on 568 adult patients with newly diagnosed diabetes. Participants were screened for islet autoantibodies to exclude those with autoimmune diabetes. The remaining participants (with type 2 diabetes) were then classified as lean (BMI <25 kg/m2) or non-lean (BMI ≥25 kg/m2), and their socio-demographic, clinical, biophysical and metabolic characteristics were compared.
.
. Results
. Thirty-four participants (6.4%) were excluded from analyses because they were positive for pancreatic autoantibodies, and a further 34 participants because they had incomplete data. For the remaining 500 participants, the median (IQR) age, BMI and HbA1c were 48 years (39–58), 27.5 kg/m2 (23.6–31.4) and 90 mmol/mol (61–113) (10.3% [7.7–12.5]), respectively, with a female predominance (approximately 57%). of the 500 participants, 160 (32%) and 340 (68%) were lean and non-lean, respectively. Compared with non-lean participants, lean participants were mainly male (60.6% vs 35.3%, p<0.001) and had lower visceral adiposity level (5 [4–7] vs 11 [9–13], p<0.001) and features of the metabolic syndrome (uric acid, 246.5 [205.0–290.6] vs 289 [234–347] μmol/l, p<0.001; leptin, 660.9 [174.5–1993.1] vs 3988.0 [1336.0–6595.0] pg/ml, p<0.001). In addition, they displayed markedly reduced markers of beta cell function (oral insulinogenic index 0.8 [0.3–2.5] vs 1.6 [0.6–4.6] pmol/mmol; 120 min serum C-peptide 0.70 [0.33–1.36] vs 1.02 [0.60–1.66] nmol/l, p<0.001).
.
. Conclusions/interpretation
. Approximately one-third of participants with incident adult-onset non-autoimmune diabetes had BMI <25 kg/m2. Diabetes in these lean individuals was more common in men, and predominantly associated with reduced pancreatic secretory function rather than insulin resistance. The underlying pathological mechanisms are unclear, but this is likely to have important management implications.
.
. Graphical abstract
.
.

Recent epidemiological data have shown that more than half of all new cases of type 1 diabetes occur in adults. Key genetic, immune, and metabolic differences exist between adult- and childhood-onset type 1 diabetes, many of which are not well understood. A substantial risk of misclassification of diabetes type can result. Notably, some adults with type 1 diabetes may not require insulin at diagnosis, their clinical disease can masquerade as type 2 diabetes, and the consequent misclassification may result in inappropriate treatment. In response to this important issue, JDRF convened a workshop of international experts in November 2019. Here, we summarize the current understanding and unanswered questions in the field based on those discussions, highlighting epidemiology and immunogenetic and metabolic characteristics of adult-onset type 1 diabetes as well as disease-associated comorbidities and psychosocial challenges. In adult-onset, as compared with childhood-onset, type 1 diabetes, HLA-associated risk is lower, with more protective genotypes and lower genetic risk scores; multiple diabetes-associated autoantibodies are decreased, though GADA remains dominant. Before diagnosis, those with autoantibodies progress more slowly, and at diagnosis, serum C-peptide is higher in adults than children, with ketoacidosis being less frequent. Tools to distinguish types of diabetes are discussed, including body phenotype, clinical course, family history, autoantibodies, comorbidities, and C-peptide. By providing this perspective, we aim to improve the management of adults presenting with type 1 diabetes.

IntroductionLow high-density lipoprotein (HDL) is widely used as a marker of cardiovascular disease risk, although this relationship is not causal and is likely mediated through associations with other risk factors. Low HDL is extremely common in sub-Saharan African populations, and this has often been interpreted to indicate that these populations will have increased cardiovascular risk. We aimed to determine whether the association between HDL and other cardiovascular risk factors differed between populations in sub-Saharan Africa and the UK.MethodsWe compared data from adults living in Uganda and Malawi (n=26 216) and in the UK (n=8747). We examined unadjusted and adjusted levels of HDL and applied the WHO recommended cut-offs for prevalence estimates. We used spline and linear regression to assess the relationship between HDL and other cardiovascular risk factors.ResultsHDL was substantially lower in the African than in the European studies (geometric mean 0.9–1.2 mmol/L vs 1.3–1.8 mmol/L), with African prevalence of low HDL as high as 77%. Total cholesterol was also substantially lower (geometric mean 3.3–3.9 mmol/L vs 4.6–5.4 mmol/L). In comparison with European studies the relationship between HDL and adiposity (body mass index, waist to hip ratio) was greatly attenuated in African studies and the relationship with non-HDL cholesterol reversed: in African studies low HDL was associated with lower non-HDL cholesterol. The association between sex and HDL was also different; using the WHO sex-specific definitions, low HDL was substantially more common among women (69%–77%) than men (41%–59%) in Uganda/Malawi.ConclusionThe relationship between HDL and sex, adiposity and non-HDL cholesterol in sub-Saharan Africa is different from European populations. In sub-Saharan Africans low HDL is a marker of low overall cholesterol and sex differences are markedly attenuated. Therefore low HDL in isolation is unlikely to indicate raised cardiovascular risk and the WHO sex-based cut-offs are inappropriate.

Abstract
. Background
. Type 2 diabetes (T2D) is common and increasing in prevalence. It is possible to prevent or delay T2D using lifestyle intervention programmes. Entry to these programmes is usually determined by a measure of glycaemia in the ‘intermediate’ range. This paper investigated the relationship between HbA1c and future diabetes risk and determined the impact of varying thresholds to identify those at high risk of developing T2D.
.
. Methods
. We studied 4227 participants without diabetes aged ≥ 40 years recruited to the Exeter 10,000 population cohort in South West England. HbA1c was measured at study recruitment with repeat HbA1c available as part of usual care. Absolute risk of developing diabetes within 5 years, defined by HbA1c ≥ 48 mmol/mol (6.5%), according to baseline HbA1c, was assessed by a flexible parametric survival model.
.
. Results
. The overall absolute 5-year risk (95% CI) of developing T2D in the cohort was 4.2% (3.6, 4.8%). This rose to 7.1% (6.1, 8.2%) in the 56% (n = 2358/4224) of participants classified ‘high-risk’ with HbA1c ≥ 39 mmol/mol (5.7%; ADA criteria). Under IEC criteria, HbA1c ≥ 42 mmol/mol (6.0%), 22% (n = 929/4277) of the cohort was classified high-risk with 5-year risk 14.9% (12.6, 17.2%). Those with the highest HbA1c values (44–47 mmol/mol [6.2–6.4%]) had much higher 5-year risk, 26.4% (22.0, 30.5%) compared with 2.1% (1.5, 2.6%) for 39–41 mmol/mol (5.7–5.9%) and 7.0% (5.4, 8.6%) for 42–43 mmol/mol (6.0–6.1%). Changing the entry criterion to prevention programmes from 39 to 42 mmol/mol (5.7–6.0%) reduced the proportion classified high-risk by 61%, and increased the positive predictive value (PPV) from 5.8 to 12.4% with negligible impact on the negative predictive value (NPV), 99.6% to 99.1%. Increasing the threshold further, to 44 mmol/mol (6.2%), reduced those classified high-risk by 59%, and markedly increased the PPV from 12.4 to 23.2% and had little impact on the NPV (99.1% to 98.5%).
.
. Conclusions
. A large proportion of people are identified as high-risk using current thresholds. Increasing the risk threshold markedly reduces the number of people that would be classified as high-risk and entered into prevention programmes, although this must be balanced against cases missed. Raising the entry threshold would allow limited intervention opportunities to be focused on those most likely to develop T2D.
.

IntroductionThe utility of HbA1c (glycosylated hemoglobin) to estimate glycemic control in populations of African and other low-resource countries has been questioned because of high prevalence of other medical conditions that may affect its reliability. Using continuous glucose monitoring (CGM), we aimed to determine the comparative performance of HbA1c, fasting plasma glucose (FPG) (within 5 hours of a meal) and random non-fasting glucose (RPG) in assessing glycemic burden.Research design and methodsWe assessed the performance of HbA1c, FPG and RPG in comparison to CGM mean glucose in 192 Ugandan participants with type 2 diabetes. Analysis was undertaken in all participants, and in subgroups with and without medical conditions reported to affect HbA1c reliability. We then assessed the performance of FPG and RPG, and optimal thresholds, in comparison to HbA1c in participants without medical conditions thought to alter HbA1c reliability.Results32.8% (63/192) of participants had medical conditions that may affect HbA1c reliability: anemia 9.4% (18/192), sickle cell trait and/or hemoglobin C (HbC) 22.4% (43/192), or renal impairment 6.3% (12/192). Despite high prevalence of medical conditions thought to affect HbA1c reliability, HbA1c had the strongest correlation with CGM measured glucose in day-to-day living (0.88, 95% CI 0.84 to 0.91), followed by FPG (0.82, 95% CI 0.76 to 0.86) and RPG (0.76, 95% CI 0.69 to 0.81). Among participants without conditions thought to affect HbA1c reliability, FPG and RPG had a similar diagnostic performance in identifying poor glycemic control defined by a range of HbA1c thresholds. FPG of ≥7.1 mmol/L and RPG of ≥10.5 mmol/L correctly identified 78.2% and 78.8%, respectively, of patients with an HbA1c of ≥7.0%.ConclusionsHbA1c is the optimal test for monitoring glucose control even in low-income and middle-income countries where medical conditions that may alter its reliability are prevalent; FPG and RPG are valuable alternatives where HbA1c is not available.

Latent autoimmune diabetes of adults (LADA) is typically defined as a new diabetes diagnosis after 35 years of age, presenting with clinical features of type 2 diabetes, in whom a type 1 diabetes–associated islet autoantibody is detected. Identifying autoimmune diabetes is important since the prognosis and optimal therapy differ. However, the existing LADA definition identifies a group with clinical and genetic features intermediate between typical type 1 and type 2 diabetes. It is unclear whether this is due to 1) true autoimmune diabetes with a milder phenotype at older onset ages that initially appears similar to type 2 diabetes but later requires insulin, 2) a disease syndrome where the pathophysiologies of type 1 and type 2 diabetes are both present in each patient, or 3) a heterogeneous group resulting from difficulties in classification. Herein, we suggest that difficulties in classification are a major component resulting from defining LADA using a diagnostic test—islet autoantibody measurement—with imperfect specificity applied in low-prevalence populations. This yields a heterogeneous group of true positives (autoimmune type 1 diabetes) and false positives (nonautoimmune type 2 diabetes). For clinicians, this means that islet autoantibody testing should not be undertaken in patients who do not have clinical features suggestive of autoimmune diabetes: in an adult without clinical features of type 1 diabetes, it is likely that a single positive antibody will represent a false-positive result. This is in contrast to patients with features suggestive of type 1 diabetes, where false-positive results will be rare. For researchers, this means that current definitions of LADA are not appropriate for the study of autoimmune diabetes in later life. Approaches that increase test specificity, or prior likelihood of autoimmune diabetes, are needed to avoid inclusion of participants who have nonautoimmune (type 2) diabetes. Improved classification will allow improved assignment of prognosis and therapy as well as an improved cohort in which to analyze and better understand the detailed pathophysiological components acting at onset and during disease progression in late-onset autoimmune diabetes.

OBJECTIVE: We investigated the processes underlying glycemic deterioration in type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: a total of 732 recently diagnosed patients with T2D from the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) study were extensively phenotyped over 3 years, including measures of insulin sensitivity (OGIS), β-cell glucose sensitivity (GS), and insulin clearance (CLIm) from mixed meal tests, liver enzymes, lipid profiles, and baseline regional fat from MRI. The associations between the longitudinal metabolic patterns and HbA1c deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression. RESULTS: Faster HbA1c progression was independently associated with faster deterioration of OGIS and GS and increasing CLIm; visceral or liver fat, HDL-cholesterol, and triglycerides had further independent, though weaker, roles (R 2 = 0.38). A subgroup of patients with a markedly higher progression rate (fast progressors) was clearly distinguishable considering these variables only (discrimination capacity from area under the receiver operating characteristic = 0.94). The proportion of fast progressors was reduced from 56% to 8-10% in subgroups in which only one trait among OGIS, GS, and CLIm was relatively stable (odds ratios 0.07-0.09). T2D polygenic risk score and baseline pancreatic fat, glucagon-like peptide 1, glucagon, diet, and physical activity did not show an independent role. CONCLUSIONS: Deteriorating insulin sensitivity and β-cell function, increasing insulin clearance, high visceral or liver fat, and worsening of the lipid profile are the crucial factors mediating glycemic deterioration of patients with T2D in the initial phase of the disease. Stabilization of a single trait among insulin sensitivity, β-cell function, and insulin clearance may be relevant to prevent progression.

INTRODUCTION: Zinc transporter 8 autoantibodies (ZnT8A) are biomarkers of beta cell autoimmunity in type 1 diabetes that have become more widely available to clinicians in recent years. Robust control population-defined thresholds are essential to ensure high clinical specificity in islet autoantibody testing. We aimed to determine the optimal cut-offs for ZnT8A testing. RESEARCH DESIGN AND METHODS: 97.5th and 99th centile cut-offs were determined using residual clinical sera from 1559 controls aged between 0 and 83 years with no history of diabetes and a hemoglobin A1c level of less than 6.0% (

The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment1. The origins of specific compounds are known, including metabolites that are highly heritable2,3, or those that are influenced by the gut microbiome4, by lifestyle choices such as smoking5, or by diet6. However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites—in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts7,8 that were not available to us when we trained the algorithms. We used feature attribution analysis9 to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites.

Abstract
Background
The diagnosis of hyperglycaemia in sub-Saharan Africa (SSA) is challenging. Blood glucose levels obtained during oral glucose tolerance test (OGTT) may not reflect home glycaemic profiles. We compare OGTT results with home glycaemic profiles obtained using the FreeStyle Libre continuous glucose monitoring device (FSL-CGM).

Methods
Twenty-eight women (20 with gestational diabetes [GDM], 8 controls) were recruited following OGTT between 24 and 28 weeks of gestation. All women wore the FSL-CGM device for 48–96 h at home in early third trimester, and recorded a meal diary. OGTT was repeated on the final day of FSL-CGM recording. OGTT results were compared with ambulatory glycaemic variables, and repeat OGTT was undertaken whilst wearing FSL-CGM to determine accuracy of the device.

Results
FSL-CGM results were available for 27/28 women with mean data capture 92.8%. There were significant differences in the ambulatory fasting, post-prandial peaks, and mean glucose between controls in whom both primary and secondary OGTT was normal (n = 6) and those with two abnormal OGTTs or “true” GDM (n = 7). There was no difference in ambulatory mean glucose between these controls and the 13 women who had an abnormal primary OGTT and normal repeat OGTT. These participants had significantly lower body mass index (BMI) than the true GDM group (29.0 Vs 36.3 kg/m2, p-value 0.014).
Paired OGTT/FSL-CGM readings revealed a Mean Absolute difference (MAD) -0.58 mmol/L and Mean Absolute Relative Difference (MARD) -11.9%. Bland-Altman plot suggests FSL-CGM underestimated blood glucose by approximately 0.78 mmol/L.

Conclusion
Diagnosis of GDM on a single OGTT identifies a proportion of women who do not have a significantly higher home glucose levels than controls. This raises questions about factors which may affect the reproducibility of OGTT in this population, including food insecurity and atypical phenotypes of diabetes. More investigation is needed to understand the suitability of the OGTT as a diagnostic test in sub-Saharan Africa.

Patients with type 1 and type 2 diabetes have very different treatment and care requirements. Overlapping phenotypes and lack of clear classification guidelines make it difficult for clinicians to differentiate between type 1 and type 2 diabetes at diagnosis. The rate of glycaemic deterioration is highly variable in patients with type 2 diabetes but there is no single test to accurately identify which patients will progress rapidly to requiring insulin therapy. Incorrect treatment and care decisions in diabetes can have life-threatening consequences.
The aim of this thesis is to develop clinical prediction models that can be incorporated into routine clinical practice to assist clinicians with the classification and care of patient diagnosed with diabetes. We addressed the problem first by integrating features previously associated with classification of type 1 and type 2 diabetes to develop a diagnostic model using logistic regression to identify, at diagnosis, patients with type 1 diabetes. The high performance achieved by this model was comparable to that of machine learning algorithms.
In patients diagnosed with type 2 diabetes, we found that patients who were GADA positive and had genetic susceptibility to type 1 diabetes progressed more rapidly to requiring insulin therapy. We built upon this finding to develop a prognostic model integrating predictive features of glycaemic deterioration to predict early insulin requirement in adults diagnosed with type 2 diabetes.
The three main findings of this thesis have the potential to change the way that patients with diabetes are managed in clinical practice.
Use of the diagnostic model developed to identify patients with type 1 diabetes has the potential to reduce misclassification. Classifying patients according to the model has the benefit of being more akin to the treatment needs of the patient rather than the aetiopathological definitions used in current clinical guidelines. The design of the model lends itself to implementing a triage-based approach to diabetes subtype diagnosis.
Our second main finding alters the clinical implications of a positive GADA test in patients diagnosed with type 2 diabetes. For identifying patients likely to progress rapidly to insulin, genetic testing is only beneficial in patients who test positive for GADA. In clinical practice, a two-step screening process could be implemented - only patients who test positive for GADA in the first step would go on for genetic testing.
The prognostic model can be used in clinical practice to predict a patient’s rate of glycaemic deterioration leading to a requirement for insulin. The availability of this data will enable clinical practices to more effectively manage their patient lists, prioritising more intensive follow up for those patients who are at high risk of rapid progression. Patients are likely to benefit from tailored treatment. Another key clinical use of the prognostic model is the identification of patients who would benefit most from GADA testing saving both inconvenience to the patient and a cost-benefit to the health service.

Abstract
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.

IntroductionTo identify risk factors, absolute risk, and impact on treatment discontinuation of genital infections with sodium-glucose co-transporter-2 inhibitors (SGLT2i).Research design and methodsWe assessed the relationship between baseline characteristics and genital infection in 21 004 people with type 2 diabetes initiating SGLT2i and 55 471 controls initiating dipeptidyl peptidase-4 inhibitors (DPP4i) in a UK primary care database. We assessed absolute risk of infection in those with key risk factors and the association between early genital infection and treatment discontinuation.ResultsGenital infection was substantially more common in those treated with SGLT2i (8.1% within 1 year) than DPP4i (1.8%). Key predictors of infection with SGLT2i were female sex (HR 3.64; 95% CI 3.23 to 4.11) and history of genital infection; <1 year before initiation (HR 4.38; 3.73 to 5.13), 1–5 years (HR 3.04; 2.64 to 3.51), and >5 years (HR 1.79; 1.55 to 2.07). Baseline HbA1c was not associated with infection risk for SGLT2i, in contrast to DPP4i where risk increased with higher HbA1c. One-year absolute risk of genital infection with SGLT2i was highest for those with a history of prior infection (females 23.7%, males 12.1%), compared with those without (females 10.8%, males 2.7%). Early genital infection was associated with a similar discontinuation risk for SGLT2i (HR 1.48; 1.21–1.80) and DPP4i (HR 1.58; 1.21–2.07).ConclusionsFemale sex and history of prior infection are simple features that can identify subgroups at greatly increased risk of genital infections with SGLT2i therapy. These data can be used to risk-stratify patients. High HbA1c is not a risk factor for genital infections with SGLT2i.

IntroductionPharmaceutical treatment options for patients with type 2 diabetes mellitus (T2DM) have increased to include multiple classes of oral glucose-lowering agents but without accompanying guidance on which of these may most benefit individual patients. Clinicians lack information for treatment intensification after first-line metformin therapy. Stratifying patients by simple clinical characteristics may improve care by targeting treatment options to those in whom they are most effective. This academically designed and run three-way crossover trial aims to test a stratification approach using three standard oral glucose-lowering agents.Methods and analysisTriMaster is a randomised, double-blind, crossover trial taking place at up to 25 clinical sites across England, Scotland and Wales. 520 patients with T2DM treated with either metformin alone, or metformin and a sulfonylurea who have glycated haemoglobin (HbA1c) >58 mmol/mol will be randomised to receive 16 weeks each of a dipeptidyl peptidase‐4 inhibitor, sodium-glucose co-transporter-2 inhibitor and thiazolidinedione in random order. Participants will be assessed at the end of each treatment period, providing clinical and biochemical data, and their experience of side effects. Participant preference will be assessed on completion of all three treatments. The primary endpoint is HbA1c after 4 months of therapy (allowing a range of 12–18 weeks for analysis). Secondary endpoints include participant-reported preference between the three treatments, tolerability and prevalence of side effects.Ethical approvalThis study was approved by National Health Service Health Research Authority Research Ethics Committee South Central—Oxford A, study 16/SC/0147. Written informed consent will be obtained from all participants. Results will be submitted to a peer-reviewed journal and presented at relevant scientific meetings. A lay summary of results will be made available to all participants.Trial registration numbers12039221; 2015-002790-38 and NCT02653209.

AbstractBackgroundThe rising prevalence of type 2 diabetes (T2D) poses a major global challenge. It remains unresolved to what extent transcriptomic signatures of metabolic dysregulation and T2D can be observed in easily accessible tissues such as blood. Additionally, large-scale human studies are required to further our understanding of the putative inflammatory component of insulin resistance and T2D. Here we used transcriptomics data from individuals with (n = 789) and without (n = 2127) T2D from the IMI-DIRECT cohorts to describe the co-expression structure of whole blood that mainly reflects processes and cell types of the immune system, and how it relates to metabolically relevant clinical traits and T2D.MethodsClusters of co-expressed genes were identified in the non-diabetic IMI-DIRECT cohort and evaluated with regard to stability, as well as preservation and rewiring in the cohort of individuals with T2D. We performed functional and immune cell signature enrichment analyses, and a genome-wide association study to describe the genetic regulation of the modules. Phenotypic and trans-omics associations of the transcriptomic modules were investigated across both IMI-DIRECT cohorts.ResultsWe identified 55 whole blood co-expression modules, some of which clustered in larger super-modules. We identified a large number of associations between these transcriptomic modules and measures of insulin action and glucose tolerance. Some of the metabolically linked modules reflect neutrophil-lymphocyte ratio in blood while others are independent of white blood cell estimates, including a module of genes encoding neutrophil granule proteins with antibacterial properties for which the strongest associations with clinical traits and T2D status were observed. Through the integration of genetic and multi-omics data, we provide a holistic view of the regulation and molecular context of whole blood transcriptomic modules. We furthermore identified an overlap between genetic signals for T2D and co-expression modules involved in type II interferon signaling.ConclusionsOur results offer a large-scale map of whole blood transcriptomic modules in the context of metabolic disease and point to novel biological candidates for future studies related to T2D.

OBJECTIVE: to develop and validate multivariable clinical diagnostic models to assist distinguishing between type 1 and type 2 diabetes in adults aged 18-50. DESIGN: Multivariable logistic regression analysis was used to develop classification models integrating five pre-specified predictor variables, including clinical features (age of diagnosis, body mass index) 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: UK cohorts recruited from primary and secondary care. PARTICIPANTS: 1352 (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 600 pmol/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. 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

BACKGROUND: 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 five clusters in the ADOPT trial (n=4351) using the same data-driven cluster analysis as reported by Ahlqvist and colleagues. Differences between clusters in glycaemic and renal progression were investigated and contrasted with stratification using simple continuous clinical features (age at diagnosis for glycaemic progression and baseline renal function for renal progression). We compared the effectiveness 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 cohort (RECORD [n=4447]). FINDINGS: Clusters identified in trial data were similar to those described in the original study by Ahlqvist and colleagues. Clusters showed differences in glycaemic progression, but a model using age at diagnosis alone explained a similar amount of variation in progression. We found differences in incidence of chronic kidney disease between clusters; however, estimated glomerular filtration rate at baseline was a better predictor of time to chronic kidney disease. Clusters differed in glycaemic response, with a particular benefit for thiazolidinediones in patients in the severe insulin-resistant diabetes cluster and for sulfonylureas in patients in the mild age-related diabetes cluster. 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 that are based on simple continuous clinical features are more useful to stratify patients. This finding suggests that precision medicine in type 2 diabetes is likely to have most clinical utility if it is based on an approach of using specific phenotypic measures to predict specific outcomes, rather than assigning patients to subgroups. FUNDING: UK Medical Research Council.

Background & Aims: Excess liver iron content is common and is linked to the risk of hepatic and extrahepatic diseases. We aimed to identify genetic variants influencing liver iron content and use genetics to understand its link to other traits and diseases. Methods: First, we performed a genome-wide association study (GWAS) in 8,289 individuals from UK Biobank, whose liver iron level had been quantified by magnetic resonance imaging, before validating our findings in an independent cohort (n = 1,513 from IMI DIRECT). Second, we used Mendelian randomisation to test the causal effects of 25 predominantly metabolic traits on liver iron content. Third, we tested phenome-wide associations between liver iron variants and 770 traits and disease outcomes. Results: We identified 3 independent genetic variants (rs1800562 [C282Y] and rs1799945 [H63D] in HFE and rs855791 [V736A] in TMPRSS6) associated with liver iron content that reached the GWAS significance threshold (p

AIMS: Most people with Type 1 diabetes have low levels of persistent endogenous insulin production. The Diabetes Control and Complications Trial showed that close to diagnosis preserved endogenous insulin was associated with lower HbA1c , hypoglycaemia and complication rates, when intensively treated. We aimed to assess the clinical impact of persistent C-peptide on rate of hypoglycaemia and HbA1c in those with long duration (> 5 years) Type 1 diabetes. METHODS: We conducted a cross-sectional case-control study of 221 people (median age 24 years) with Type 1 diabetes. We confirmed ongoing endogenous insulin secretion by measuring C-peptide after a mixed-meal tolerance test. We compared self-reported hypoglycaemia (n = 160), HbA1c , insulin dose and microvascular complications (n = 140) in those with preserved and low C-peptide. RESULTS: Stimulated median (IQR) C-peptide was 114 (43, 273) pmol/l and

AIM: to describe population-level time trends in prescribing patterns of type 2 diabetes therapy, and in short-term clinical outcomes (glycated haemoglobin [HbA1c], weight, blood pressure, hypoglycaemia and treatment discontinuation) after initiating new therapy. MATERIALS AND METHODS: We studied 81 532 people with type 2 diabetes initiating a first- to fourth-line drug in primary care between 2010 and 2017 inclusive in United Kingdom electronic health records (Clinical Practice Research Datalink). Trends in new prescriptions and subsequent 6- and 12-month adjusted changes in glycaemic response (reduction in HbA1c), weight, blood pressure and rates of hypoglycaemia and treatment discontinuation were examined. RESULTS: Use of dipeptidyl peptidase-4 inhibitors as second-line therapy near doubled (41% of new prescriptions in 2017 vs. 22% in 2010), replacing sulphonylureas as the most common second-line drug (29% in 2017 vs. 53% in 2010). Sodium-glucose co-transporter-2 inhibitors, introduced in 2013, comprised 17% of new first- to fourth-line prescriptions by 2017. First-line use of metformin remained stable (91% of new prescriptions in 2017 vs. 91% in 2010). Over the study period there was little change in average glycaemic response and in the proportion of people discontinuing treatment. There was a modest reduction in weight after initiating second- and third-line therapy (improvement in weight change 2017 vs. 2010 for second-line therapy: -1.5 kg, 95% confidence interval [CI] -1.9, -1.1; P 

AIMS/HYPOTHESIS: Late-onset type 1 diabetes can be difficult to identify. Measurement of endogenous insulin secretion using C-peptide provides a gold standard classification of diabetes type in longstanding diabetes that closely relates to treatment requirements. We aimed to determine the prevalence and characteristics of type 1 diabetes defined by severe endogenous insulin deficiency after age 30 and assess whether these individuals are identified and managed as having type 1 diabetes in clinical practice. METHODS: We assessed the characteristics of type 1 diabetes defined by rapid insulin requirement (within 3 years of diagnosis) and severe endogenous insulin deficiency (non-fasting C-peptide 600 pmol/l) and 220 participants with severe insulin deficiency who were diagnosed under age 30. RESULTS: Twenty-one per cent of participants with insulin-treated diabetes who were diagnosed after age 30 met the study criteria for type 1 diabetes. of these participants, 38% did not receive insulin at diagnosis, of whom 47% self-reported type 2 diabetes. Rapid insulin requirement was highly predictive of severe endogenous insulin deficiency: 85% required insulin within 1 year of diagnosis, and 47% of all those initially treated without insulin who progressed to insulin treatment within 3 years of diagnosis had severe endogenous insulin deficiency. Participants with late-onset type 1 diabetes defined by development of severe insulin deficiency had similar clinical characteristics to those with young-onset type 1 diabetes. However, those with later onset type 1 diabetes had a modestly lower type 1 diabetes genetic risk score (0.268 vs 0.279; p 

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.

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 > 58 mmol/mol (7.5%). For those with no HbA1c improvement or a limited response at 6 months (HbA1c fall

OBJECTIVE: Precision medicine drug therapy seeks to maximize efficacy and minimize harm for individual patients. This will be difficult if drug response and side effects are positively associated, meaning that 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 the risk of side effects (survival outcome) for patients initiating type 2 diabetes therapy. STUDY DESIGN AND SETTING: Participants were randomized to metformin (MFN), sulfonylurea (SU), or thiazolidinedione (TZD) therapy in the a Diabetes Outcome Progression Trial (ADOPT) drug efficacy trial (n=4,351). Joint models were parameterized for 1) current HbA1c response (change from baseline in HbA1c) and 2) cumulative HbA1c response (total HbA1c change). RESULTS: with MFN, greater HbA1c response did not increase the risk of gastrointestinal events (HR per 1% absolute greater current response 0.82 [95% CI 0.67, 1.01]; HR per 1% higher cumulative response 0.90 [95% CI 0.81, 1.00]). With SU, greater current response was associated with an increased risk of hypoglycemia (HR 1.41 [95% CI 1.04, 1.91]). With TZD, greater response was associated with an increased risk of edema (current HR 1.45 [95% CI 1.05, 2.01]; cumulative 1.22 [95% CI 1.07, 1.38]) but not fracture. CONCLUSION: Joint modeling provides a useful framework to evaluate the association between response to a drug and the risk of developing side effects. There may be great potential for widespread application of joint modeling to evaluate the risks and benefits of both new and established medications.

Aims/hypothesis: the aim of this study was to determine whether random non-fasting C-peptide (rCP) measurement can be used to assess hypoglycaemia risk in insulin-treated type 2 diabetes. Methods: We compared continuous glucose monitoring-assessed SD of blood glucose and hypoglycaemia duration in 17 patients with insulin-treated type 2 diabetes and severe insulin deficiency (rCP  600 pmol/l). We then assessed the relationship between rCP and questionnaire-based measures of hypoglycaemia in 256 patients with insulin-treated type 2 diabetes and a comparison group of 209 individuals with type 1 diabetes. Results: Continuous glucose monitoring (CGM)-assessed glucose variability and hypoglycaemia was greater in individuals with rCP 

Aims: to measure the variation in prescribing of second-line non-insulin diabetes drugs. Materials and Methods: We evaluated time trends for the period 1998 to 2016, using England's publicly available prescribing datasets, and stratified these by the order in which they were prescribed to patients using the Clinical Practice Research Datalink. We calculated the proportion of each class of diabetes drug as a percentage of the total per year. We evaluated geographical variation in prescribing using general practice-level data for the latest 12 months (to August 2017), with aggregation to Clinical Commissioning Groups. We calculated percentiles and ranges, and plotted maps. Results: Prescribing of therapy after metformin is changing rapidly. Dipeptidyl peptidase-4 (DPP-4) inhibitor use has increased markedly, with DPP-4 inhibitors now the most common second-line drug (43% prescriptions in 2016). The use of sodium-glucose co-transporter-2 (SGLT-2) inhibitors also increased rapidly (14% new second-line, 27% new third-line prescriptions in 2016). There was wide geographical variation in choice of therapies and average spend per patient. In contrast, metformin was consistently used as a first-line treatment in accordance with guidelines. Conclusions: in England there is extensive geographical variation in the prescribing of diabetes drugs after metformin, and increasing use of higher-cost DPP-4 inhibitors and SGLT-2 inhibitors compared with low-cost sulphonylureas. Our findings strongly support the case for comparative effectiveness trials of current diabetes drugs.

Samples for adrenocorticotropic hormone (ACTH) and aldosterone/renin analysis usually require rapid transport to the receiving laboratory for immediate separation and freezing. In practice, this means assessment is limited to hospital settings and many samples are rejected. We examined whether these requirements are necessary by assessing the stability of ACTH, aldosterone and renin over 48 hours in whole blood collected in serum gel and EDTA plasma from 31 participants. Our results show that ACTH collected into EDTA plasma is stable at room temperature for at least 6 hours, mean change at 6 hours -2.6% (95% CI -9.7 to 4.5). Both aldosterone and renin were stable collected on serum gel at room temperature for at least 6 hours: mean change aldosterone +0.2% (95% CI -3.6 to 4.0), renin -1.9% (95% CI -7.0 to3.2). Therefore, by using appropriate preservatives, ACTH and aldosterone/renin can be measured on samples collected at room temperature and processed within 6 hours. This would facilitate outpatient and emergency room assessment of these analytes.

OBJECTIVE: with rising obesity, it is becoming increasingly difficult to distinguish between type 1 diabetes (T1D) and type 2 diabetes (T2D) in young adults. There has been substantial recent progress in identifying the contribution of common genetic variants to T1D and T2D. We aimed to determine whether a score generated from common genetic variants could be used to discriminate between T1D and T2D and also to predict severe insulin deficiency in young adults with diabetes. RESEARCH DESIGN AND METHODS: We developed genetic risk scores (GRSs) from published T1D- and T2D-associated variants. We first tested whether the scores could distinguish clinically defined T1D and T2D from the Wellcome Trust Case Control Consortium (WTCCC) (n = 3,887). We then assessed whether the T1D GRS correctly classified young adults (diagnosed at 20-40 years of age, the age-group with the most diagnostic difficulty in clinical practice; n = 223) who progressed to severe insulin deficiency 0.280 (>50th centile in those with T1D) is indicative of T1D (50% sensitivity, 95% specificity). A low T1D GRS (

OBJECTIVE: to assess whether clinical characteristics and simple biomarkers of β-cell failure are associated with individual variation in glycemic response to GLP-1 receptor agonist (GLP-1RA) therapy in patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: We prospectively studied 620 participants with type 2 diabetes and HbA1c ≥58 mmol/mol (7.5%) commencing GLP-1RA therapy as part of their usual diabetes care and assessed response to therapy over 6 months. We assessed the association between baseline clinical measurements associated with β-cell failure and glycemic response (primary outcome HbA1c change 0-6 months) with change in weight (0-6 months) as a secondary outcome using linear regression and ANOVA with adjustment for baseline HbA1c and cotreatment change. RESULTS: Reduced glycemic response to GLP-1RAs was associated with longer duration of diabetes, insulin cotreatment, lower fasting C-peptide, lower postmeal urine C-peptide-to-creatinine ratio, and positive GAD or IA2 islet autoantibodies (P ≤ 0.01 for all). Participants with positive autoantibodies or severe insulin deficiency (fasting C-peptide ≤0.25 nmol/L) had markedly reduced glycemic response to GLP-1RA therapy (autoantibodies, mean HbA1c change -5.2 vs. -15.2 mmol/mol [-0.5 vs. -1.4%], P = 0.005; C-peptide

BACKGROUND: Differentiating between type 1 and type 2 diabetes is fundamental to ensuring appropriate management of patients, but can be challenging, especially when treating with insulin. The 2010 UK Practical Classification Guidelines for Diabetes were developed to help make the differentiation. AIM: to assess diagnostic accuracy of the UK guidelines against 'gold standard' definitions of type 1 and type 2 diabetes based on measured C-peptide levels. DESIGN AND SETTING: in total, 601 adults with insulin-treated diabetes and diabetes duration ≥5 years were recruited in Devon, Northamptonshire, and Leicestershire. METHOD: Baseline information and home urine sample were collected. Urinary C-peptide creatinine ratio (UCPCR) measures endogenous insulin production. Gold standard type 1 diabetes was defined as continuous insulin treatment within 3 years of diagnosis and absolute insulin deficiency (UCPCR

BackgroundMeasuring endogenous insulin secretion using C-peptide can assist diabetes management, but standard stimulation tests are impractical for clinical use. Random non-fasting C-peptide assessment would allow testing when a patient is seen in clinic.MethodsWe compared C-peptide at 90 min in the mixed meal tolerance test (sCP) with random non-fasting blood C-peptide (rCP) and random non-fasting urine C-peptide creatinine ratio (rUCPCR) in 41 participants with insulin-treated diabetes [median age 72 (interquartile range 68-78); diabetes duration 21 (14-31) years]. We assessed sensitivity and specificity for previously reported optimal mixed meal test thresholds for severe insulin deficiency (sCP < 200 pmol//l) and Type 1 diabetes/inability to withdraw insulin (< 600 pmol//l), and assessed the impact of concurrent glucose.ResultsrCP and sCP levels were similar (median 546 and 487 pmol//l, P = 0.92). rCP was highly correlated with sCP, r = 0.91, P < 0.0001, improving to r = 0.96 when excluding samples with concurrent glucose < 8 mmol//l. An rCP cut-off of 200 pmol//l gave 100% sensitivity and 93% specificity for detecting severe insulin deficiency, with area under the receiver operating characteristic curve of 0.99. rCP < 600 pmol//l gave 87% sensitivity and 83% specificity to detect sCP < 600 pmol//l. Specificity improved to 100% when excluding samples with concurrent glucose < 8 mmol//l. rUCPCR (0.52 nmol/mmol) was also well-correlated with sCP, r = 0.82, P < 0.0001. A rUCPCR cut-off of < 0.2 nmol/ mmol gave sensitivity and specificity of 83% and 93% to detect severe insulin deficiency, with area under the receiver operating characteristic curve of 0.98.ConclusionsRandom non-fasting C-peptide measures are strongly correlated with mixed meal C-peptide, and have high sensitivity and specificity for identifying clinically relevant thresholds. These tests allow assessment of C-peptide at the point patients are seen for clinical care.

AIMS: Baseline HbA1c is a major predictor of response to glucose lowering therapy and therefore a potential confounder in studies aiming to identify other predictors. However, baseline adjustment may introduce error if the association between baseline HbA1c and response is substantially due to measurement error and regression to the mean. We aimed to determine whether studies of predictors of response should adjust for baseline HbA1c. METHODS: We assessed the relationship between baseline HbA1c and glycaemic response in 257 participants treated with GLP-1R agonists and assessed whether it reflected measurement error and regression to the mean using duplicate 'pre-baseline' HbA1c measurements not included in the response variable. In this cohort and an additional 2659 participants treated with sulfonylureas we assessed the relationship between covariates associated with baseline HbA1c and treatment response with and without baseline adjustment, and with a bias correction using pre-baseline HbA1c to adjust for the effects of error in baseline HbA1c. RESULTS: Baseline HbA1c was a major predictor of response (R2 = 0.19,β = -0.44,p

OBJECTIVE: Clinicians predominantly use clinical features to differentiate type 1 from type 2 diabetes yet there are no evidence-based clinical criteria to aid classification of patients. Misclassification of diabetes is widespread (7-15% of cases), resulting in patients receiving inappropriate treatment. We sought to identify which clinical criteria could be used to discriminate type 1 and type 2 diabetes. DESIGN: Systematic review of all diagnostic accuracy studies published since 1979 using clinical criteria to predict insulin deficiency (measured by C-peptide). DATA SOURCES: 14 databases including: MEDLINE, MEDLINE in Process and EMBASE. The search strategy took the form of: (terms for diabetes) AND (terms for C-Peptide). ELIGIBILITY CRITERIA: Diagnostic accuracy studies of any routinely available clinical predictors against a reference standard of insulin deficiency defined by cut-offs of C-peptide concentrations. No restrictions on race, age, language or country of origin. RESULTS: 10,917 abstracts were screened, and 231 full texts reviewed. 11 studies met inclusion criteria, but varied by age, race, year and proportion of participants who were C-peptide negative. Age at diagnosis was the most discriminatory feature in 7/9 studies where it was assessed, with optimal cut-offs (>70% mean sensitivity and specificity) across studies being

AIMS: Defining responders to glucose lowering therapy can be important for both clinical care and for the development of a stratified approach to diabetes management. Response is commonly defined by either HbA1c change after treatment or whether a target HbA1c is achieved. We aimed to determine the extent to which the individuals identified as responders and non-responders to glucose lowering therapy, and their characteristics, depend on the response definition chosen. METHODS: We prospectively studied 230 participants commencing GLP-1 agonist therapy. We assessed participant characteristics at baseline and repeated HbA1c after 3 months treatment. We defined responders (best quartile of response) based on HbA1c change or HbA1c achieved. We assessed the extent to which these methods identified the same individuals and how this affected the baseline characteristics associated with treatment response. RESULTS: Different definitions of response identified different participants. Only 39% of responders by one definition were also good responders by the other. Characteristics associated with good response depend on the response definition chosen: good response by HbA1c achieved was associated with low baseline HbA1c (p

Aims/hypothesis: Classically, type 1 diabetes is thought to proceed to absolute insulin deficiency. Recently developed ultrasensitive assays capable of detecting C-peptide under 5 pmol/l now allow very low levels of C-peptide to be detected in patients with long-standing type 1 diabetes. It is not known whether this low-level endogenous insulin secretion responds to physiological stimuli. We aimed to assess how commonly low-level detectable C-peptide occurs in long-duration type 1 diabetes and whether it responds to a meal stimulus. Methods: We performed a mixed-meal tolerance test in 74 volunteers with long-duration (>5 years) type 1 diabetes, i.e. with age at diagnosis 16 (9-23) years (median [interquartile range]) and diabetes duration of 30 (19-41) years. We assessed fasting and stimulated serum C-peptide levels using an electrochemiluminescence assay (detection limit 3.3 pmol/l), and also the urinary C-peptide:creatinine ratio (UCPCR). Results: Post-stimulation serum C-peptide was detectable at very low levels (>3.3 pmol/l) in 54 of 74 (73%) patients. In all patients with detectable serum C-peptide, C-peptide either increased (n = 43, 80%) or stayed the same (n = 11) in response to a meal, with no indication of levels falling (p < 0.0001). With increasing disease duration, absolute C-peptide levels fell although the numbers with detectable C-peptide remained high (68%, i.e. 25 of 37 patients with >30 years duration). Similar results were obtained for UCPCR. Conclusions/interpretation: Most patients with long-duration type 1 diabetes continue to secrete very low levels of endogenous insulin, which increase after meals. This is consistent with the presence of a small number of still functional beta cells and implies that beta cells are either escaping immune attack or undergoing regeneration. © 2013 the Author(s).

C-peptide is produced in equal amounts to insulin and is the best measure of endogenous insulin secretion in patients with diabetes. Measurement of insulin secretion using C-peptide can be helpful in clinical practice: differences in insulin secretion are fundamental to the different treatment requirements of Type 1 and Type 2 diabetes. This article reviews the use of C-peptide measurement in the clinical management of patients with diabetes, including the interpretation and choice of C-peptide test and its use to assist diabetes classification and choice of treatment. We provide recommendations for where C-peptide should be used, choice of test and interpretation of results. With the rising incidence of Type 2 diabetes in younger patients, the discovery of monogenic diabetes and development of new therapies aimed at preserving insulin secretion, the direct measurement of insulin secretion may be increasingly important. Advances in assays have made C-peptide measurement both more reliable and inexpensive. In addition, recent work has demonstrated that C-peptide is more stable in blood than previously suggested or can be reliably measured on a spot urine sample (urine C-peptide:creatinine ratio), facilitating measurement in routine clinical practice. The key current clinical role of C-peptide is to assist classification and management of insulin-treated patients. Utility is greatest after 3-5 years from diagnosis when persistence of substantial insulin secretion suggests Type 2 or monogenic diabetes. Absent C-peptide at any time confirms absolute insulin requirement and the appropriateness of Type 1 diabetes management strategies regardless of apparent aetiology. © 2013 Diabetes UK.

Aims: to determine the prevalence and clinical characteristics of absolute insulin deficiency in long-standing Type 2 diabetes, using a strategy based on home urinary C-peptide creatinine ratio measurement. Methods: We assessed the urinary C-peptide creatinine ratios, from urine samples taken at home 2 h after the largest meal of the day, in 191 insulin-treated subjects with Type 2 diabetes (diagnosis age ≥45 years, no insulin in the first year). If the initial urinary C-peptide creatinine ratio was ≤0.2 nmol/mmol (representing absolute insulin deficiency), the assessment was repeated. A standardized mixed-meal tolerance test with 90-min stimulated serum C-peptide measurement was performed in nine subjects with a urinary C-peptide creatinine ratio ≤ 0.2 nmol/mmol (and in nine controls with a urinary C-peptide creatinine ratio >0.2 nmol/mmol) to confirm absolute insulin deficiency. Results: a total of 2.7% of participants had absolute insulin deficiency confirmed by a mixed-meal tolerance test. They were identified initially using urinary C-peptide creatinine ratio: 11/191 subjects (5.8%) had two consistent urinary C-peptide creatinine ratios ≤ 0.2 nmol/mmol; 9 of these 11 subjects completed a mixed-meal tolerance test and had a median stimulated serum C-peptide of 0.18 nmol/l. Five of these 9 had stimulated serum C-peptide 0.2 had endogenous insulin secretion confirmed by the mixed-meal tolerance test. Compared with subjects with a urinary C-peptide creatinine ratio >0.2 nmol/mmol, those with confirmed absolute insulin deficiency had a shorter time to insulin treatment (median 2.5 vs. 6 years, P=0.005) and lower BMI (25.1 vs. 29.1 kg/m2, P=0.04). Two out of the five patients with absolute insulin deficiency were glutamic acid decarboxylase autoantibody-positive. Conclusions: Absolute insulin deficiency may occur in long-standing Type 2 diabetes, and cannot be reliably predicted by clinical features or autoantibodies. Absolute insulin deficiency in Type 2 diabetes may increase the risk of hypoglycaemia and ketoacidosis, as in Type 1 diabetes. Its recognition should help guide treatment, education and management. The urinary C-peptide creatinine ratio is a practical non-invasive method to aid detection of absolute insulin deficiency, with a urinary C-peptide creatinine ratio > 0.2 nmol/mmol being a reliable indicator of retained endogenous insulin secretion. © 2013 the Authors. Diabetic Medicine published by John Wiley & Sons Ltd on behalf of Diabetes UK.

AIMS: to determine the prevalence and clinical characteristics of absolute insulin deficiency in long-standing Type 2 diabetes, using a strategy based on home urinary C-peptide creatinine ratio measurement. METHODS: We assessed the urinary C-peptide creatinine ratios, from urine samples taken at home 2 h after the largest meal of the day, in 191 insulin-treated subjects with Type 2 diabetes (diagnosis age ≥45 years, no insulin in the first year). If the initial urinary C-peptide creatinine ratio was ≤0.2 nmol/mmol (representing absolute insulin deficiency), the assessment was repeated. A standardized mixed-meal tolerance test with 90-min stimulated serum C-peptide measurement was performed in nine subjects with a urinary C-peptide creatinine ratio ≤ 0.2 nmol/mmol (and in nine controls with a urinary C-peptide creatinine ratio >0.2 nmol/mmol) to confirm absolute insulin deficiency. RESULTS: a total of 2.7% of participants had absolute insulin deficiency confirmed by a mixed-meal tolerance test. They were identified initially using urinary C-peptide creatinine ratio: 11/191 subjects (5.8%) had two consistent urinary C-peptide creatinine ratios ≤ 0.2 nmol/mmol; 9 of these 11 subjects completed a mixed-meal tolerance test and had a median stimulated serum C-peptide of 0.18 nmol/l. Five of these 9 had stimulated serum C-peptide 0.2 had endogenous insulin secretion confirmed by the mixed-meal tolerance test. Compared with subjects with a urinary C-peptide creatinine ratio >0.2 nmol/mmol, those with confirmed absolute insulin deficiency had a shorter time to insulin treatment (median 2.5 vs. 6 years, P=0.005) and lower BMI (25.1 vs. 29.1 kg/m(2) , P=0.04). Two out of the five patients with absolute insulin deficiency were glutamic acid decarboxylase autoantibody-positive. CONCLUSIONS: Absolute insulin deficiency may occur in long-standing Type 2 diabetes, and cannot be reliably predicted by clinical features or autoantibodies. Absolute insulin deficiency in Type 2 diabetes may increase the risk of hypoglycaemia and ketoacidosis, as in Type 1 diabetes. Its recognition should help guide treatment, education and management. The urinary C-peptide creatinine ratio is a practical non-invasive method to aid detection of absolute insulin deficiency, with a urinary C-peptide creatinine ratio > 0.2 nmol/mmol being a reliable indicator of retained endogenous insulin secretion.

Background: in patients with both Type 1 and Type 2 diabetes endogenous insulin secretion falls with time which changes treatment requirements, however direct measurement of endogenous insulin secretion is rarely performed. We aimed to assess the impact of endogenous insulin secretion on postprandial glucose increase and the effectiveness of prandial exogenous insulin.Methods: We assessed endogenous insulin secretion in 102 participants with insulin treated diabetes (58 Type 1) following a standardised mixed meal without exogenous insulin. We tested the relationship between endogenous insulin secretion and post meal hyperglycaemia. In 80 participants treated with fast acting breakfast insulin we repeated the mixed meal with participants' usual insulin given and assessed the impact of endogenous insulin secretion on response to exogenous prandial insulin.Results: Post meal glucose increment (90 minute - fasting) was inversely correlated with endogenous insulin secretion (90 minute C-peptide) (Spearman's r = -0.70, p < 0.001). Similar doses of exogenous prandial insulin lowered glucose increment more when patients had less endogenous insulin; by 6.4(4.2-11.1) verses 1.2(0.03-2.88) mmol/L (p < 0.001) for patients in the lowest verses highest tertiles of endogenous insulin.Conclusions: in insulin treated patients the measurement of endogenous insulin secretion may help predict the degree of postprandial hyperglycaemia and the likely response to prandial insulin. © 2012 Jones et al.; licensee BioMed Central Ltd.

BACKGROUND: Urinary C-peptide creatinine ratio (UCPCR) is a non-invasive and convenient way of assessing endogenous insulin production. Adjusting for urine creatinine levels allows for differences in urine concentration. Creatinine excretion is known to be higher in men due to gender differences in muscle mass. We investigated the impact of gender on UCPCR. METHODS: One hundred and seventy-six subjects underwent a mixed meal tolerance test (MMTT). We looked at the relationship between UCPCR on urine C-peptide and creatinine excretion rates using timed post-meal urine samples. A further 415 subjects had two-hour post-meal UCPCR measurements in order to derive gender-specific percentiles for different diabetes subgroups and controls. RESULTS: UCPCR was 1.48-fold higher in women (n=78) than men (n=98), median (interquartile range [IQR]): 1.88 (0.49-3.49) men versus 2.88 (1.58-4.91) nmol mmol(-1) women, P=0.01. This reflects a gender difference in creatinine excretion rates (11.5 [8.3-13.7] men versus 8.2 [5.6-9.1] women μmol min(-1) P

AIMS: the mixed meal tolerance test is the gold standard measure of endogenous insulin secretion. Practical issues limit the routine clinical use of this test, including omitting insulin prior to the ingestion of a high-carbohydrate liquid mixed meal, which can result in marked hyperglycaemia. We aimed to assess whether insulin omission is necessary during the mixed meal tolerance test and whether fasting C-peptide was a practical alternative to the test. METHODS: Ninety-one adults with insulin-treated diabetes (Type 1 n = 56, Type 2 n = 35) underwent two mixed meal tolerance tests; one standard without insulin and one with the patient's usual morning insulin. RESULTS: the 90-min serum C-peptide was highly correlated in the standard mixed meal tolerance test and the test with insulin (r = 0.98, P < 0.0001). There was a 20% reduction in the peak C-peptide value when insulin was given {test with insulin [0.39 (0.01-1.16) vs. test without insulin 0.48 (0.01-1.36) nmol/l, P = 0.001]}, but the original serum C-peptide cut-off for significant endogenous insulin secretion (≥ 0.2 nmol/l) still correctly classified 90/91 patients (98% sensitivity/100% specificity). Fasting serum C-peptide was highly correlated to 90-min serum C-peptide during the test (r = 0.97, P < 0.0001). A fasting serum C-peptide ≥ 0.07 nmol/l was the optimal cut-off (100% sensitivity and 97% specificity) for significant endogenous insulin secretion (defined as 90-min stimulated serum C-peptide ≥ 0.2 nmol/l). CONCLUSIONS: Insulin omission may not always be necessary during a mixed meal tolerance test and fasting serum C-peptide may offer a practical alternative in insulin-treated patients.

A 19. year old male attended his GP with a history of "fluid retention", lack of libido and erectile dysfunction. He was found to have a high serum testosterone, and a raised luteinising hormone. After further investigations, the patient admitted to taking a supplement called ActivaTe Xtreme™, obtained from an internet source, to address his low libido.ActivaTe Xtreme™ contains active ingredients which increase serum testosterone levels by several independent mechanisms that are not associated with luteinising hormone suppression. Urine analyses for synthetic anabolic steroids were negative, and urinary testosterone, epitestosterone and other androgens were normal. This biochemical pattern is not the same as that seen with anabolic steroids (i.e. raised testosterone, suppressed luteinising hormone and abnormal urine steroid profile).The issue of self medication with performance enhancing compounds needs to be carefully considered in order to avoid expensive and invasive investigations, missing an underlying pathology or misdiagnosing a patient. This case also raises the spectre of yet another "performance enhancing" product that may cause difficulty for those trying to ensure that sport remains on a "hormonally" equal basis. © 2011.

OBJECTIVE: Stimulated serum C-peptide (sCP) during a mixed-meal tolerance test (MMTT) is the gold standard measure of endogenous insulin secretion, but practical issues limit its use. We assessed urine C-peptide creatinine ratio (UCPCR) as an alternative. RESEARCH DESIGN AND METHODS: Seventy-two type 1 diabetic patients (age of diagnosis median 14 years [interquartile range 10-22]; diabetes duration 6.5 [2.3-32.7]) had an MMTT. sCP was collected at 90 min. Urine for UCPCR was collected at 120 min and following a home evening meal. RESULTS: MMTT 120-min UCPCR was highly correlated to 90-min sCP (r = 0.97; P < 0.0001). UCPCR ≥ 0.53 nmol/mmol had 94% sensitivity/100% specificity for significant endogenous insulin secretion (90-min sCP ≥ 0.2 nmol/L). The 120-min postprandial evening meal UCPCR was highly correlated to 90-min sCP (r = 0.91; P < 0.0001). UCPCR ≥ 0.37 nmol/mmol had 84% sensitivity/97% specificity for sCP ≥ 0.2 nmol/L. CONCLUSIONS: UCPCR testing is a sensitive and specific method for detecting insulin secretion. UCPCR may be a practical alternative to serum C-peptide testing, avoiding the need for inpatient investigation.

AIMS: Serum C-peptide measurement can assist clinical management of diabetes, but practicalities of collection limit widespread use. Urine C-peptide creatinine ratio may be a non-invasive practical alternative. The stability of C-peptide in urine allows outpatient or community testing. We aimed to assess how urine C-peptide creatinine ratio compared with serum C-peptide measurement during a mixed-meal tolerance test in individuals with late-onset, insulin-treated diabetes. METHODS: We correlated the gold standard of a stimulated serum C-peptide in a mixed-meal tolerance test with fasting and stimulated (mixed-meal tolerance test, standard home meal and largest home meal) urine C-peptide creatinine ratio in 51 subjects with insulin-treated diabetes (diagnosis after age 30 years, median age 66 years, median age at diagnosis 54, 42 with Type 2 diabetes, estimated glomerular filtration rate > 60 ml min(-1) 1.73 m(-2) ). RESULTS: Ninety-minute mixed-meal tolerance test serum C-peptide is correlated with mixed-meal tolerance test-stimulated urine C-peptide creatinine ratio (r = 0.82), urine C-peptide creatinine ratio after a standard breakfast at home (r = 0.73) and urine C-peptide creatinine ratio after largest home meal (r = 0.71). A stimulated (largest home meal) urine C-peptide creatinine ratio cut-off of 0.3 nmol/mmol had a 100% sensitivity and 96% specificity (area under receiver operating characteristic curve = 0.99) in identifying subjects without clinically significant endogenous insulin secretion (mixed-meal tolerance test-stimulated C-peptide < 0.2 nmol/l). In detecting a proposed serum C-peptide threshold for insulin requirement (stimulated serum C-peptide < 0.6 nmol/l), a stimulated (largest home meal) urine C-peptide creatinine ratio cut-off of 0.6 nmol/mmol had a sensitivity and specificity of 92%. CONCLUSION: in patients with insulin-treated diabetes diagnosed after age 30 years, urine C-peptide creatinine ratio is well correlated with serum C-peptide and may provide a practical alternative measure to detect insulin deficiency for use in routine clinical practice.

BACKGROUND: a 17-year-old female was referred for the reassessment of her type 1 diabetes mellitus, with which she had been diagnosed at the age of 15 weeks owing to symptoms of ketoacidosis. The patient had mild learning difficulties, which resulted in her requiring additional support at school. There was no family history of diabetes. INVESTIGATIONS: Measurements of plasma C-peptide and glutamate decarboxylase autoantibodies. Molecular genetic testing was performed. DIAGNOSIS: Intermediate developmental delay, epilepsy and neonatal diabetes mellitus (DEND) syndrome as a result of a 59V>M Kir6.2 mutation. MANAGEMENT: Treatment with high-dose oral glibenclamide replaced insulin treatment. Good glycemic control was achieved with levels of HbA(1c) consistently below 6.5% and no hypoglycemia.

Lethal necrotizing fasciitis caused by Streptococcus pyogenes is characterized by a paucity of neutrophils at the site of infection. Interleukin (IL)-8, which is important for neutrophil transmigration and activation, can be degraded by S. pyogenes. Blood isolates of S. pyogenes were better able to degrade human IL-8 than throat isolates. Degradation of IL-8 was the result of a single specific cleavage between 59glutamine and 60arginine within the IL-8 C-terminal alpha helix. Cleaved IL-8 reduced neutrophil activation and migration. IL-8-cleaving activity was found in partially purified supernatant of a necrotizing fasciitis isolate, and this activity was associated with an approximately 150-kDa fraction containing S. pyogenes cell envelope proteinase (SpyCEP). IL-8-cleaving activity corresponded with the presence of SpyCEP in the supernatant. Cleavage of IL-8 by S. pyogenes represents an unprecedented mechanism of immune evasion, effectively preventing IL-8 C-terminus-mediated endothelial translocation and subsequent recruitment of neutrophils.