AbstractData linkage of cohort or RCT data with routinely collected data is becoming increasingly commonplace, and this often involves combining biomarker measurements from different sources. However, sources may have different biases due to differences in assay method and calibration. Combining these measurements, or diagnoses based on these measurements, is therefore not always valid. We highlight an example using glycated haemoglobin A1c (HbA1c) test results from two different sources in UK Biobank data.

AbstractBackgroundThis study evaluates longitudinal associations between glycaemic control (mean and within-patient variability of glycated haemaglobin (HbA1c) levels) in individuals with type 2 diabetes (T2D) and major depressive disorder (MDD).MethodsIn UK Biobank (UKB), T2D was defined using self-report, linked primary care records, prescription records and hospital episode statistics, then validated using polygenic scores. Repeated HbA1c measurements were extracted from primary care records and baseline UKB biomarker measures, and used as the outcome in mixed effects models to investigate the association between MDD and glycaemic control over a maximum 10-year T2D disease duration. The exposure investigated was MDD, with subgroups defined by the relative timings of MDD and T2D diagnoses (no MDD, MDD diagnosis pre-T2D or post-T2D).Multiple imputation (n=9264) and complete case (n=4233) analyses were performed.ResultsThe T2D diagnostic criteria were robustly associated with T2D polygenic scores. Using mixed effect models and multiple imputation (7.6 year median follow-up), temporal trends in mean HbA1c did not differ by MDD subgroup. Within-patient variability in HbA1c was 1.14 (95% CI: 1.12-1.16) times higher in UKB participants diagnosed with MDD after T2D compared those with no MDD diagnosis. Complete case analyses results differed, but the substantial evidence against missingness being completely at random, suggests that complete case analysis will be biased.ConclusionsThese findings suggest closer monitoring to improve T2D control is important in individuals who develop MDD after T2D diagnosis. Further, the study highlights the importance of correctly accounting for missing data.Key messagesMajor depressive disorder (MDD) is associated with poorer glycaemic control and an increased risk of complications and mortality in individuals with type 2 diabetes (T2D). Research suggests that individuals diagnosed with MDD after T2D (post-T2D MDD) may be driving these associations, but the effect of diagnostic order on glycaemic control has not been investigated longitudinally.Using UK Biobank primary care records, we investigated whether post-T2D MDD is associated with poorer glycaemic control over the 10 years following a T2D diagnosis, as measured by mean HbA1c trends and within-patient HbA1c variability.Individuals with post-T2D MDD had greater within-patient variability in HbA1c compared to those with no MDD diagnosis, indicating poorer glycaemic control, but no difference in mean HbA1c was found. Individuals with pre-existing MDD had similar glycaemic control to those with no MDD.Results suggest the importance of considering within-patient variability in HbA1c, in addition to the mean, when investigating glycaemic control over time, and highlight the role of the relative timing of diagnosis for MDD and T2D in glycaemic control.

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.

BackgroundFabry disease is an X-linked lysosomal storage disorder resulting from deficiency of the alpha-galactosidase a enzyme leading to accumulation of globotriaosylceramide in multiple organ sites with prominent cardiovascular and renal involvement. Global prevalence estimates of Fabry disease based on clinical ascertainment range from 1 in 40 000 to 1 in 170 000. We aimed to determine the prevalence of Fabry disease-causing variants in UK Biobank.MethodsWe soughtGLAgene variants in exome sequencing data from 200 643 individuals from UK Biobank. We used ACMG/AMP guidelines (American College of Medical Genetics/Association for Molecular Pathology) to classify pathogenicity and compared baseline biomarker data, hospital ICD-10 (International Classification of Diseases version-10) codes, general practitioner records and self-reported health data with those without pathogenic variants.ResultsWe identified 81GLAcoding variants. We identified eight likely pathogenic variants on the basis of being rare (<1/10 000 individuals) and either previously reported to cause Fabry disease, or being protein-truncating variants. Thirty-six individuals carried one of these variants. In the UK Biobank, the prevalence of likely pathogenic Fabry disease-causing variants is 1/5732 for late-onset disease-causing variants and 1/200 643 for variants causing classic Fabry disease.ConclusionFabry disease-causingGLAvariants are more prevalent in an unselected population sample than the reported prevalence of Fabry disease. These are overwhelmingly variants associated with later onset. It is possible the prevalence of later-onset Fabry disease exceeds current estimates.

AbstractData linkage of cohort or RCT data with routinely collected data is becoming increasingly commonplace, and this often involves combining biomarker measurements from different sources. However, sources may have different biases due to differences in assay method and calibration. Combining these measurements, or diagnoses based on these measurements, is therefore not always valid. We highlight an example using glycated haemoglobin A1c (HbA1c) test results from two different sources in UK Biobank data.

AbstractA precision medicine approach in type 2 diabetes (T2D) could enhance targeting specific glucose-lowering therapies to individual patients most likely to benefit. We utilised Bayesian non-parametric modelling to develop and validate an individualised treatment selection algorithm for two major T2D drug classes, SGLT2-inhibitors (SGLT2i) and GLP1-receptor agonists (GLP1-RA). The algorithm is designed to predict differences in 12-month glycaemic outcome (HbA1c) between the 2 therapies, based on routine clinical features of 46,394 people with T2D in England (27,319 for model development, 19,075 for hold-out validation), with additional external validation in 2,252 people with T2D from Scotland. Routine clinical features, including sex (with females markedly more responsive to GLP1-RA), were associated with differences in glycaemic outcomes. Our algorithm identifies clearly delineable subgroups with reproducible ≥5mmol/mol HbA1cbenefits associated with each drug class. Moreover, we demonstrate that targeting the therapies based on predicted glycaemic response is associated with improvements in short-term tolerability and long-term risk of new-onset microvascular complications. These results show that precision medicine approaches to T2D can facilitate effective individualised treatment selection, and that use of routinely collected clinical features could support low-cost deployment in many countries.

ABSTRACTBackgroundA precision medicine approach in type 2 diabetes requires identification of clinical and biological features that are reproducibly associated with differences in clinical outcomes with specific anti-hyperglycaemic therapies. Robust evidence of such treatment effect heterogeneity could support more individualized clinical decisions on optimal type 2 diabetes therapy.MethodsWe performed a pre-registered systematic review of meta-analysis studies, randomized control trials, and observational studies evaluating clinical and biological features associated with heterogenous treatment effects for SGLT2-inhibitor and GLP1-receptor agonist therapies, considering glycaemic, cardiovascular, and renal outcomes.ResultsAfter screening 5,686 studies, we included 101 studies of SGLT2-inhibitors and 75 studies of GLP1-receptor agonists in the final systematic review. The majority of papers had methodological limitations precluding robust assessment of treatment effect heterogeneity. For glycaemic outcomes, most cohorts were observational, with multiple analyses identifying lower renal function as a predictor of lesser glycaemic response with SGLT2-inhibitors and markers of reduced insulin secretion as predictors of lesser response with GLP1-receptor agonists. For cardiovascular and renal outcomes, the majority of included studies were post-hoc analyses of randomized control trials (including meta-analysis studies) which identified limited clinically relevant treatment effect heterogeneity.ConclusionsCurrent evidence on treatment effect heterogeneity for SGLT2-inhibitor and GLP1-receptor agonist therapies is limited, likely reflecting the methodological limitations of published studies. Robust and appropriately powered studies are required to understand type 2 diabetes treatment effect heterogeneity and evaluate the potential for precision medicine to inform future clinical care.Plain language summaryThis review identifies research that helps understand which clinical and biological factors that are associated with different outcomes for specific type 2 diabetes treatments. This information could help clinical providers and patients make better informed personalized decisions about type 2 diabetes treatments. We focused on two common type 2 diabetes treatments: SGLT2-inhibitors and GLP1-receptor agonists, and three outcomes: blood glucose control, heart disease, and kidney disease. We identified some potential factors that are likely to lessen blood glucose control including lower kidney function for SGLT2-inhibitors and lower insulin secretion for GLP1-receptor agonists. We did not identify clear factors that alter heart and renal disease outcomes for either treatment. Most of the studies had limitations, meaning more research is needed to fully understand the factors that influence treatment outcomes in type 2 diabetes.

AbstractRecent type 2 diabetes guidance from the UK’s National Institute for Health and Care Excellence (NICE) proposes offering SGLT2-inhibitor therapy to people with established atherosclerotic cardiovascular disease (ASCVD) or heart failure, or at high-risk of cardiovascular disease defined as a 10-year cardiovascular risk of >10% using the QRISK2 algorithm.We used a contemporary population-representative UK cohort of people with type 2 diabetes to assess the implications of this guidance. 93.1% of people currently on anti-hyperglycaemic treatment are now recommended or considered for SGLT2-inhibitor therapy, with the majority (59.6%) eligible on the basis of QRISK2 rather than established ASCVD or heart failure (33.6%). Applying these results to the approximately 2.20 million people in England currently on anti-hyperglycaemic medication suggests 1.75 million people will now be considered for additional SGLT2-inhibitor therapy.Given older people, those of non-white ethnic groups, and those at lower cardiovascular disease risk were underrepresented in the clinical trials upon which this guidance was based, careful evaluation of the impact and safety of increased SGLT2-inhibitor prescribing across different populations is urgently required. Evidence of benefit should be weighed against the major cost implications for the NHS.

AbstractBackgroundThis study evaluates longitudinal associations between glycaemic control (mean and within-patient variability of glycated haemaglobin (HbA1c) levels) in individuals with type 2 diabetes (T2D) and major depressive disorder (MDD).MethodsIn UK Biobank (UKB), T2D was defined using self-report, linked primary care records, prescription records and hospital episode statistics, then validated using polygenic scores. Repeated HbA1c measurements were extracted from primary care records and baseline UKB biomarker measures, and used as the outcome in mixed effects models to investigate the association between MDD and glycaemic control over a maximum 10-year T2D disease duration. The exposure investigated was MDD, with subgroups defined by the relative timings of MDD and T2D diagnoses (no MDD, MDD diagnosis pre-T2D or post-T2D).Multiple imputation (n=9264) and complete case (n=4233) analyses were performed.ResultsThe T2D diagnostic criteria were robustly associated with T2D polygenic scores. Using mixed effect models and multiple imputation (7.6 year median follow-up), temporal trends in mean HbA1c did not differ by MDD subgroup. Within-patient variability in HbA1c was 1.14 (95% CI: 1.12-1.16) times higher in UKB participants diagnosed with MDD after T2D compared those with no MDD diagnosis. Complete case analyses results differed, but the substantial evidence against missingness being completely at random, suggests that complete case analysis will be biased.ConclusionsThese findings suggest closer monitoring to improve T2D control is important in individuals who develop MDD after T2D diagnosis. Further, the study highlights the importance of correctly accounting for missing data.Key messagesMajor depressive disorder (MDD) is associated with poorer glycaemic control and an increased risk of complications and mortality in individuals with type 2 diabetes (T2D). Research suggests that individuals diagnosed with MDD after T2D (post-T2D MDD) may be driving these associations, but the effect of diagnostic order on glycaemic control has not been investigated longitudinally.Using UK Biobank primary care records, we investigated whether post-T2D MDD is associated with poorer glycaemic control over the 10 years following a T2D diagnosis, as measured by mean HbA1c trends and within-patient HbA1c variability.Individuals with post-T2D MDD had greater within-patient variability in HbA1c compared to those with no MDD diagnosis, indicating poorer glycaemic control, but no difference in mean HbA1c was found. Individuals with pre-existing MDD had similar glycaemic control to those with no MDD.Results suggest the importance of considering within-patient variability in HbA1c, in addition to the mean, when investigating glycaemic control over time, and highlight the role of the relative timing of diagnosis for MDD and T2D in glycaemic control.

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.

BackgroundFabry disease is an X-linked lysosomal storage disorder resulting from deficiency of the alpha-galactosidase a enzyme leading to accumulation of globotriaosylceramide in multiple organ sites with prominent cardiovascular and renal involvement. Global prevalence estimates of Fabry disease based on clinical ascertainment range from 1 in 40 000 to 1 in 170 000. We aimed to determine the prevalence of Fabry disease-causing variants in UK Biobank.MethodsWe soughtGLAgene variants in exome sequencing data from 200 643 individuals from UK Biobank. We used ACMG/AMP guidelines (American College of Medical Genetics/Association for Molecular Pathology) to classify pathogenicity and compared baseline biomarker data, hospital ICD-10 (International Classification of Diseases version-10) codes, general practitioner records and self-reported health data with those without pathogenic variants.ResultsWe identified 81GLAcoding variants. We identified eight likely pathogenic variants on the basis of being rare (<1/10 000 individuals) and either previously reported to cause Fabry disease, or being protein-truncating variants. Thirty-six individuals carried one of these variants. In the UK Biobank, the prevalence of likely pathogenic Fabry disease-causing variants is 1/5732 for late-onset disease-causing variants and 1/200 643 for variants causing classic Fabry disease.ConclusionFabry disease-causingGLAvariants are more prevalent in an unselected population sample than the reported prevalence of Fabry disease. These are overwhelmingly variants associated with later onset. It is possible the prevalence of later-onset Fabry disease exceeds current estimates.

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.
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. 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.
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. 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.
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. 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.
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. Graphical abstract
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AbstractAimsChange in weight, HbA1c, lipids, blood pressure and cardiometabolic events over time is variable in individuals with type 2 diabetes. We hypothesised that people with a genetic predisposition to a more favourable adiposity distribution could have a less severe clinical course/progression.MethodsWe involved people with type 2 diabetes from two UK‐based cohorts: 11,914 individuals with GP follow‐up data from the UK Biobank and 723 from Salford. We generated a ‘favourable adiposity’ genetic score and conducted cross‐sectional and longitudinal studies to test its association with weight, BMI, lipids, blood pressure, medication use and risk of myocardial infarction and stroke using 15 follow‐up time points with 1‐year intervals.ResultsThe ‘favourable adiposity’ genetic score was cross‐sectionally associated with higher weight (effect size per 1 standard deviation higher genetic score: 0.91 kg [0.59,1.23]) and BMI (0.30 kg/m2 [0.19,0.40]), but higher high‐density lipoprotein (0.02 mmol/L [0.01,0.02]) and lower triglycerides (−0.04 mmol/L [−0.07, −0.02]) in the UK Biobank at baseline, and this pattern of association was consistent across follow‐up.There was a trend for participants with higher ‘favourable adiposity’ genetic score to have lower risk of myocardial infarction and/or stroke (odds ratio 0.79 [0.62, 1.00]) compared to those with lower score. A one standard deviation higher score was associated with lower odds of using lipid‐lowering (0.91 [0.86, 0.97]) and anti‐hypertensive medication (0.95 [0.91, 0.99]).ConclusionsIn individuals with type 2 diabetes, having more ‘favourable adiposity’ alleles is associated with a marginally better lipid profile long‐term and having lower odds of requiring lipid‐lowering or anti‐hypertensive medication in spite of relatively higher adiposity.

The success of DNA nanotechnology has been driven by the discovery of novel
structural motifs with a wide range of shapes and uses. We present a
comprehensive study of the T-motif, a 3-armed, planar, right-angled junction
that has been used in the self-assembly of DNA polyhedra and periodic
structures. The motif is formed through the interaction of a bulge loop in one
duplex and a sticky end of another. The polarity of the sticky end has
significant consequences for the thermodynamic and geometrical properties of
the T-motif: different polarities create junctions spanning different grooves
of the duplex. We compare experimental binding strengths with predictions of
oxDNA, a coarse-grained model of DNA, for various loop sizes. We find that,
although both sticky-end polarities can create stable junctions, junctions
resulting from 5$'$ sticky ends are stable over a wider range of bulge loop
sizes. We highlight the importance of possible coaxial stacking interactions
within the motif and investigate how each coaxial stacking interaction
stabilises the structure and favours a particular geometry.